Regional Haze State Implementation 

Plan 

for 

Tennessee Class I Areas 

Prepared by

Tennessee Department of Environment and Conservation

Division of Air Pollution Control 

April 4, 2008 
 i 

Preface: This document contains summaries of the technical analyses that will be used by the 

Tennessee Department of Environment and Conservation’s Division of Air Pollution Control 

(TDEC-APC) to support the regional haze state implementation plan pursuant to §§107(d)(3)(D) 

and (E) of the Clean Air Act, as amended. 
 ii 

EXECUTIVE SUMMARY 

Introduction 

Regional haze is pollution that impairs visibility over a large region, including national parks, 

forests, and wilderness areas (many termed “Class I” areas). Regional haze is caused by sources 

and activities emitting fine particles and their precursors, often transported over large regions. 

Particles affect visibility through the scattering and absorption of light. Reducing fine particles in 

the atmosphere is an effective method of improving visibility. In the southeast, the most 

important sources of haze-forming emissions are coal-fired power plants, industrial boilers and 

other combustion sources, but also include mobile source emissions, area sources, fires, and 

wind blown dust. 

An easily understood measure of visibility to most people is visual range. Visual range is the 

greatest distance, in kilometers or miles, at which a dark object can be viewed against the sky. 

However, the most useful measure of visibility impairment is light extinction, which affects the 

clarity and color of objects being viewed. The measure used by the regional haze rule is the 

deciview (dv), calculated directly from light extinction using a logarithmic scale. 

The regional haze rule requires states to demonstrate reasonable progress toward meeting the 

national goal of a return to natural visibility conditions by 2064. The rule directs states to 

graphically show what would be a “uniform rate of progress”, also known as the “glide path”, 

toward natural conditions for each Class I area within the State and certain ones outside the 

State. 

Tennessee’s Class I areas 

Tennessee has two Class I areas within its borders: Great Smoky Mountains National Park and 

Joyce Kilmer-Slickrock Wilderness Area. The Great Smoky Mountains National Park and Joyce 

Kilmer – Slickrock Wilderness Area are each located in both North Carolina and Tennessee. The 

figure below illustrates the location of these Class I areas. 

Visibility on the worst days at Tennessee’s Class I areas is generally 30 dv. Natural background 

visibility on the worst days is between 11 and 12 dv. 

Great Smoky Mountains National Park

Joyce Kilmer-Slickrock
 iii 

State Implementation Plan Requirements 

States are required to submit state implementation plans (SIPs) to the United States 

Environmental Protection Agency that set out each states’ plan for meeting the national goal of a 

return to natural visibility conditions by 2064. The plan includes the states’ reasonable progress 

goals, expressed in deciviews, for visibility improvement at each affected Class I area for each 

10-year period until 2064. 

SIPs must include determinations of the baseline visibility conditions (expressed in deciviews) 

for the most impaired and least impaired days. In addition, states must include a monitoring 

strategy for measuring, characterizing, and reporting of regional haze visibility impairment. The 

long-term strategy includes enforceable emissions limitations, compliance schedules, and other 

measures as necessary to achieve the reasonable progress goals. States must also consider 

ongoing control programs, measures to mitigate construction activities, source retirement and 

replacement schedules, smoke management programs for agriculture and forestry, and 

enforceability of specific measures. 

The SIPs for the first review period are due December 17, 2007. These plans will cover longterm strategies for visibility improvement between baseline conditions in 2000-2004 and 2018. 

States are required to evaluate progress toward reasonable progress goals every 5 years to assure 

that installed emissions controls are on track with emissions reduction forecasts in each SIP. 

Federal and State Control Requirements 

There are significant control programs being implemented between the baseline period and 2018. 

These programs will all reduce the particulate emissions that affect visibility in the Class I areas, 

and include: the Clean Air Interstate Rule (CAIR), the NOx SIP Call, the North Carolina Clean 

Smokestacks Act, Georgia Multi-Pollutant Rule, consent agreements with Tampa Electric, 

Virginia Electric and Power Company, American Electric Power, and Gulf Power, one-hour 

ozone SIPs submitted by Atlanta, Birmingham, and Northern Kentucky, NOx RACT in 8-hour 

nonattainment area SIPs, heavy duty diesel (2007) engine standard (for on-road trucks and 

buses), Tier 2 tailpipe standards for on-road vehicles, large spark ignition and recreational 

vehicle rule, nonroad diesel rule, and various Federal Maximum Achievable Control Technology 

regulations. 

The regional haze rule also requires states to determine best available retrofit technology 

(BART) for certain facilities. Eight of Tennessee’s twelve BART-eligible sources were able to 

demonstrate that they did not cause or contribute to visibility impairment at any Class I areas. 

Four BART sources in Tennessee are being subjected to BART limitations. Permit conditions 

for the facilities appear in Section 7.5.3, and upon approval by USEPA, will be incorporated into 

the facility’s Part 70 permit as a new applicable requirement in keeping with the reopening 

requirements of 40 CFR Part 70. The four BART sources and a brief description of their BART 

control obligations are presented below. The actual permit conditions presented in Section 7.5.3 

are the ruling limitations. The summary below is presented merely for ease of reference and 

should in no way be interpreted to supercede the permit conditions presented in Section 7.5.3: 
 iv 

1. Eastman Chemical Company in Kingsport, Sullivan County, Tennessee – This facility 

has an SO2 emission limitation/removal efficiency limitation such that post control 

combustion controls are a certainty. 

2. Alcoa in Alcoa, Blount County, Tennessee – This facility is a primary aluminum 

smelting operation that uses petroleum coke to manufacture aluminum from alumina ore. 

The sulfur content of the petroleum coke is being limited to 3 percent by weight. The 

limitation of sulfur in the coke will limit the formation of sulfur oxides. 

3. TVA Cumberland Fossil Plant in Cumberland City, Stewart County, Tennessee – This 

facility is already controlled by a flue gas desulfurization scrubber for SO2. Because of 

trading provisions allowed under the USEPA’s Acid Rain Program and its Clean Air 

Interstate Rule, an emission limitation of 0.5 pounds of SO2 per million British Thermal 

Units of heat input is being established to limit the “importation” of control credits from 

other facilities to satisfy the facility’s BART control obligations. 

4. Dupont in Old Hickory, Davidson County, Tennessee – This facility is being subjected to 

seasonal emission limitations for both SO2 and NOx. 

Conclusion 

At both Class I areas in Tennessee, visibility improvements on the worst days are expected to be 

better than the uniform rate of progress glidepath by 2018 based solely on reductions from 

existing and planned emissions controls. Additionally, the visibility is expected to improve for 

the best days for all of the Tennessee Class I areas. The table below displays the 2018 reasonable 

progress goals for the Tennessee Class I areas. 

Class I Area 

Baseline 

Visibility 

Worst Days 

Reasonable 

Progress Goal 

– Worst Days 

Baseline 

Visibility 

Best Days 

Reasonable 

Progress 

Goal – Best 

Days 

Great Smoky 

Mountains National 

Park 

30.28 23.50 13.58 12.11 

Joyce – Kilmer 

Slickrock Wilderness 

Area 

30.28 23.50 13.58 12.11 
 v 

TABLE OF CONTENTS 

1.0 INTRODUCTION ................................................................................................................... 1 

1.1 What is regional haze? .......................................................................................................... 1 

1.2 What are the requirements under the Clean Air Act for addressing regional haze? ............. 1 

1.3 General overview of regional haze SIP requirements .......................................................... 3 

1.4 Class I areas in Tennessee .................................................................................................... 4 

1.5 State and Federal Land Manager (FLM) coordination ......................................................... 7 

2.0 ASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND ESTIMATE OF 

NATURAL BACKGROUND CONDITIONS IN CLASS I AREAS ................................. 9 

2.1 Estimating Natural Conditions for Tennessee Class I Areas .............................................. 10 

2.2 Estimating Baseline Conditions for Tennessee Class I Areas ............................................ 12 

2.3 Summary of Natural Background and Baseline Conditions for Tennessee Class I Areas . 12 

2.4 Pollutant Contributions to Visibility Impairment (2000-2004 Baseline Data) ................... 13 

3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064 ................................................... 17 

3.1 Glidepaths for Class I Areas in Tennessee ......................................................................... 17 

4.0 TYPES OF EMISSIONS IMPACTING VISIBILITY IMPAIRMENT IN TENNESSEE 

CLASS I AREAS ................................................................................................................ 19 

4.1 Baseline Emissions Inventory ............................................................................................. 19 

4.1.1 Stationary Point Sources ............................................................................................ 20 

4.1.2 Stationary Area Sources ............................................................................................. 21 

4.1.3 Off-Road Mobile Sources .......................................................................................... 22 

4.1.4 Highway Mobile Sources ........................................................................................... 22 

4.1.5 Biogenic Emission Sources ........................................................................................ 22 

4.1.6 Summary 2002 Base G2 (February 2007 version) Baseline Emissions Inventory for 

Tennessee ......................................................................................................................... 23 

Source Category ........................................................................................................................ 23 

Point .......................................................................................................................................... 23 

4.1.7 Model Performance Improvements through Emissions Inventory Improvements .... 23 

4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories . 24 

5.0 REGIONAL HAZE MODELING METHODS AND INPUTS........................................... 25 

5.1 Analysis Method ................................................................................................................. 25 

5.2 Model Selection .................................................................................................................. 26 

5.2.1 Selection of Photochemical Grid Model .................................................................... 26 

5.2.2 Selection of Meteorological Model ............................................................................ 27 

5.2.3 Selection of Emissions Processing System ................................................................ 29 
 vi 

5.3 Selection of the Modeling Year .......................................................................................... 30 

5.4 Modeling Domains ............................................................................................................. 32 

5.4.1 Horizontal Modeling Domain .................................................................................... 32 

5.4.2 Vertical Modeling Domain ......................................................................................... 33 

6.0 MODEL PERFORMANCE EVALUATION ....................................................................... 33 

6.1 Modeling Performance Goals, and Criteria ........................................................................ 35 

6.2 VISTAS Domain-Wide Performance ................................................................................. 36 

6.3 Tennessee Class 1 Areas Performance ............................................................................... 39 

6.3.1 Great Smoky Mountain National Park ....................................................................... 41 

7.0 LONG-TERM STRATEGY FOR TENNESSEE CLASS I AREAS ............................... 42 

7.1 Overview of the Long-Term Strategy Development Process ............................................. 42 

7.2 Expected Visibility Results in 2018 for TN Class I Areas under existing and planned 

emissions controls ............................................................................................................ 43 

7.2.1 Federal and State Control Requirements .................................................................... 43 

7.2.2 Additional State programs to reduce emissions ......................................................... 47 

7.2.3 Projected 2009 and 2018 Emissions Inventories ........................................................ 47 

7.2.4 Model Results for the 2018 Inventory Compared to the Uniform Rate of Progress 

Glidepaths for Tennessee Class I Areas ........................................................................... 49 

7.3 Relative Contribution from International Emissions to Visibility Impairment in 2018 at 

VISTAS Class I areas ....................................................................................................... 54

7.4 Relative Contributions to Visibility Impairment: Pollutants, Source Categories, and 

Geographic Areas ............................................................................................................. 57 

7.5 What Control Determinations Represent Best Available Retrofit Technology (BART) for 

Individual Sources? .......................................................................................................... 60 

7.5.1 BART-Eligible Sources in Tennessee ...................................................................... 60 

7.5.2 Determination of Sources Subject to BART in Tennessee ........................................ 62 

7.5.3 Determination of BART Requirements for Subject-to-BART Sources ..................... 66 

7.6 Relative Contributions to Visibility Impairment: Geographic Areas of Influence for 

Tennessee Class I Areas ................................................................................................... 70 

7.6.1 Back Trajectory Analyses .......................................................................................... 71 

7.6.2 Residence Time Plots ................................................................................................. 72

7.6.3 SO2 Areas of Influence .............................................................................................. 72 

7.6.4 Emissions Sources within SO2 Areas of Influence .................................................. 73 

7.6.5 Specific Source Types in the Areas of Influence for Tennessee Class I Areas ......... 75 

7.7 Evaluating the Four Statutory Factors for Specific SO2 Emissions Sources in Each Area of 

Influence ........................................................................................................................... 79 

7.8 Which Control Measures Represent Reasonable Progress for Individual Sources? .......... 81 
 vii 

8.0 REASONABLE PROGRESS GOALS .............................................................................. 86 

9.0 MONITORING STRATEGY ............................................................................................... 88 

10.0 INTERSTATE CONSULTATION .................................................................................... 91 

11.0 COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN REVISIONS ............... 92 

12.0 DETERMINATION OF ADEQUACY OF THE EXISTING PLAN ............................... 94 

13.0 IMPACT OF TENNESSEE SOURCES ON CLASS I AREAS OUTSIDE OF 

TENNESSEE ...................................................................................................................... 95 
 viii 

Appendix A: VISTAS Memorandum of Understanding and Bylaws 

Appendix A - VISTASBylaws\Appendix A.doc

Appendix B: Conceptual Description 

Appendix B - ConceptDesc\Appendix B - Cover & Intro.doc

Appendix C: Modeling Analysis Protocol and Quality Assurance Project Plan 

Appendix C - ModProtocols&QAPP-Revised\Appendix C Header and Intro.doc

Appendix D: Emissions Preparation and Results 

Appendix D - Emissions Preparation and Results\Appendix D header.doc

Appendix E: Air Quality and Meteorological Preparations and Results 

Appendix E - AQ&MetPrep&Result\Appendix E. - Header and Intro.doc

Appendix F: Model Performance Evaluation 

Appendix F - MODPerformanceEval\Appendix F header.doc

Appendix G: Modeling Results and Supplemental Analysis 

Appendix G- MODResults&SuppAnalysis\Appendix G header.doc

Appendix H: Reasonable Progress Evaluation/Long Term Strategy 

Appendix H - RPG and LTS\Appendix H header.doc

Appendix I: Data Access 

Appendix I - Data Access\Appendix I header.doc

Appendix J: Consultation Process 

Appendix J - Consultation Process\Appendix J - header.doc

Appendix K: Recommended Improvements 

Appendix K - Improvements\Appendix K - header.doc

Appendix L: BART Related Documentation 

Appendix L - TN BART Related Documents\Appendix L header.doc

Appendix M: VISTAS Modeling Reports 

Appendix M - VISTAS Modeling\Appendix M - header.doc

Appendix N: Public Hearing Comment Summary & Agency Responses 

Appendix N - Public Record\Appendix N - Header.doc
1 

1.0 INTRODUCTION 

1.1 What is regional haze? 

Regional haze is pollution from disparate sources that impairs visibility over a large 

region, including national parks, forests, and wilderness areas (156 of which are termed 

mandatory Federal “Class I” areas). Regional haze is caused by sources and activities 

emitting fine particles and their precursors. Those emissions are often transported over 

large regions. 

Particles affect visibility through the scattering and absorption of light, and fine particles 

– particles similar in size to the wavelength of light – are most efficient, per unit of mass, 

at reducing visibility. Fine particles may either be emitted directly or formed from 

emissions of precursors, the most important of which are sulfur dioxides (SO2) and 

nitrogen oxides (NOx). Reducing fine particles in the atmosphere is generally considered 

to be an effective method of reducing regional haze, and thus improving visibility. Fine 

particles also adversely impact human health, especially respiratory and cardiovascular 

systems. The United States Environmental Protection Agency (USEPA) has set national 

ambient air quality standards for daily and annual levels of fine particles with a diameter 

smaller than 2.5 μm (PM2.5). In the southeast, the most important sources of PM2.5 and 

its precursors are coal-fired power plants, industrial boilers and other combustion 

sources. Other significant contributors to PM2.5 and visibility impairment include 

mobile source emissions, area sources, fires, and wind blown dust. 

1.2 What are the requirements under the Clean Air Act for addressing regional 

haze? 

In Section 169A of the 1977 Amendments to the Clean Air Act (CAA), Congress set 

forth a program for protecting visibility in Federal Class I areas which calls for the 

“prevention of any future, and the remedying of any existing, impairment of visibility in 

mandatory Class I Federal areas which impairment results from manmade air pollution.” 

Congress adopted the visibility provisions to protect visibility in these 156 national parks, 

forests and wilderness areas. On December 2, 1980, the USEPA promulgated regulations 

to address visibility impairment (45 FR 80084). The 1980 regulations were developed to 

address visibility impairment that is “reasonably attributable” to a single source or small 

group of sources. These regulations represented the first phase in addressing visibility 

impairment and deferred action on regional haze that emanates from a variety of sources 

until monitoring, modeling and scientific knowledge about the relationships between 

pollutants and visibility impairment improved. 

In the 1990 Amendments to the CAA, Congress added section 169B and called on 

USEPA to issue regional haze rules. The regional haze rule that USEPA promulgated on 

July 1, 1999 (64 FR 35713), revised the existing visibility regulations in order to integrate 
2 

provisions addressing regional haze impairment and establish a comprehensive visibility 

protection program for Class I Federal areas. States are required to submit state 

implementation plans (SIPs) to the USEPA that set out each states’ plan for complying 

with the regional haze rule, including consultation and coordination with other states and 

with Federal Land Managers (FLMs). The timing of SIP submittal is tied to USEPA’s 

promulgation of designations for the National Ambient Air Quality Standard (NAAQS) 

for fine particulate matter. States must submit a regional haze implementation plan to the 

USEPA within three years after the date of designation. Because the USEPA 

promulgated designation dates on December 17, 2004, regional haze SIPs must be 

submitted by December 17, 2007. 

The regional haze rule addressed the combined visibility effects of various pollution 

sources over a wide geographic region. This wide reaching pollution net meant that 

many states – even those without Class I Areas – would be required to participate in haze 

reduction efforts. The USEPA designated five Regional Planning Organizations (RPOs) 

to assist with the coordination and cooperation needed to address the visibility issue. 

The RPO that makes up the southeastern portion of the contiguous United States is 

known as VISTAS (Visibility Improvement – State and Tribal Association of the 

Southeast), and include the following states: Alabama, Florida, Georgia, Kentucky, 

Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. 

 Figure 1.2-1. Geographical Areas of Regional Planning Organizations 
3 

1.3 General overview of regional haze SIP requirements 

The regional haze rule at 51.308(d) requires states to demonstrate reasonable progress 

toward meeting the national goal of a return to natural visibility conditions by 2064. As a 

guide for reasonable progress, the regional haze rule directs states to graphically show 

what would be a “uniform rate of progress” toward natural conditions for each mandatory 

Class I Federal area within the State and/or for each mandatory Class I Federal area 

located outside the State, which may be affected by emissions from sources within the 

State. States are to establish baseline visibility conditions for 2000-2004, natural 

background visibility conditions in 2064, and the rate of uniform progress between 

baseline and background conditions. The uniform rate of progress is also known as the 

“glidepath.” 

The regional haze rule then requires States to establish reasonable progress goals, 

expressed in deciviews, for visibility improvement at each affected Class I area covering 

each (approximately) 10-year period until 2064. The goals must provide for reasonable 

progress towards achieving natural visibility conditions, provide for improvement in 

visibility for the most impaired days over the period of the implementation plan, and 

ensure no degradation in visibility for the least impaired days over the same period [see 

40 CFR 51.308(d)(1)]. 

In order to ensure that visibility goals are properly met and set, SIPs must include 

determinations, for each Class I area, of the baseline visibility conditions (expressed in 

deciviews) for the most impaired and least impaired days. SIPs must also contain 

supporting documentation for all required analyses used to calculate the degree of 

visibility impairment under natural visibility conditions for the most impaired and least 

impaired days [see 40 CFR 51.308(d)(2)]. In addition, states must include a monitoring 

strategy for measuring, characterizing, and reporting of regional haze visibility 

impairment that is representative of all mandatory Class I Federal areas within the state 

[see 40 CFR 51.308(d)(4)].

This first set of reasonable progress goals must be met through measures contained in the 

state’s long-term strategy covering the period from the present until 2018. The long-term 

strategy includes enforceable emissions limitations, compliance schedules, and other 

measures as necessary to achieve the reasonable progress goals, including all controls 

required or expected under all federal and state regulations by 2009 and by 2018. 

During development of the long-term strategy, states are also required to consider 

specific factors such as the abovementioned ongoing control programs, measures to 

mitigate construction activities, source retirement and replacement schedules, smoke 

management programs for agriculture and forestry, and enforceability of specific 

measures [see 40 CFR 51.308(d)(3)]. 

In addition, a specific component of each state’s first long-term strategy is dictated by the 

specific best available retrofit technology (BART) requirements in 40 CFR 51.308(e) of 

the regional haze rule. The regional haze rule at 40 CFR 51.308(e) requires states to 

include a determination of BART for each BART-eligible source in the State that emits 
4 

any air pollutant, which may reasonably be anticipated to cause or contribute to any 

impairment of visibility in any mandatory Class I Federal area. The Clean Air Act at 

section 169A(b) defines BART-eligible sources as sources in 26 specific source 

categories, in operation within a 15-year period prior to enactment of the 1977 Clean Air 

Act Amendments. States must determine BART according to five factors set out in 

section 169A(g)(7) of the Clean Air Act. Emission limitations representing BART and 

schedules for compliance with BART for each source subject to BART must be included 

in the long-term strategy. 

The SIPs for the first review period are due December 17, 2007. These plans will cover 

long-term strategies for visibility improvement between baseline conditions in 2000-2004 

and 2018. States are required to evaluate progress toward reasonable progress goals 

every 5 years to assure that installed emissions controls are on track with emissions 

reduction forecasts in each SIP. The first interim review would be due to USEPA in 

December 2012. If emissions controls are not on track to meet SIP forecasts, then states 

would need to take action to assure emissions controls by 2018 will be consistent with the 

SIP or to revise the SIP to be consistent with the revised emissions forecast. 

The USEPA provided several guidance documents listed below to assist the states in 

implementation of the regional haze rule requirements. Tennessee followed these 

guidance documents in developing the technical analyses reported in this plan. 

• Guidance for Tracking Progress Under the Regional Haze Rule (EPA-454/B-03-

004, September 2003). 

• Guidance for Estimating Natural Visibility Conditions Under the Regional Haze 

Rule (EPA-454/B-03-005, September 2003). 

• Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5 and Regional Haze (EPA, 

2007). 

• Guidance for Setting Reasonable Progress Goals Under the Regional Haze 

Program (EPA, June 2007). 

1.4 Class I areas in Tennessee 

Tennessee has two Class I areas within its borders: Great Smoky Mountains National 

Park and Joyce Kilmer-Slickrock Wilderness Area. TDEC-APC is responsible for 

developing the Regional Haze SIP for Tennessee and submitting it to the Tennessee Air 

Pollution Control Board for approval. This SIP establishes reasonable progress goals for 

visibility improvement at each of these Class I areas, and a long-term strategy that will 

achieve those reasonable progress goals within the first regional haze planning period. 

The Great Smoky Mountains and Joyce Kilmer – Slickrock are located in both Tennessee 

and North Carolina. For the Great Smoky Mountains, both states are sharing the lead for 

setting goals and for Joyce Kilmer – Slickrock, North Carolina is the lead. These two 
5 

Class I Areas for Tennessee are described at 40 CFR 81.428 and are shown graphically in 

Figure 1.4-1. 

Per the aforementioned federal regulation and data from the National Park Service 

regarding the Great Smoky Mountains National Park, Tennessee has 244,742 acres of the 

Park and North Carolina has 276,344 acres of the Park for a total of 521,086 acres. 

Great Smoky Mountains National Park 

TN & NC Acreage Percentages

NC 276,344 

Acres

53%

TN 244,742 

Acres

47%

With respect to the Joyce Kilmer Slickrock Wilderness Area, Tennessee has 3,832 acres 

of the Area and North Carolina has 13,562 acres of the Area for a total of 17,394 acres. 

Joyce Kilmer Slickrock Wilderness Area 

TN & NC Acreage Percentages

NC 13,562 

Acres

78%

TN 3,832 

Acres

22%

Because both of Tennessee’s Class I Areas are also shared with the State of North 

Carolina, it is fitting that the two states’ SIPs for the common Class I Areas are similar. 

Tennessee’s Regional Haze SIP follows the general outline of the North Carolina 

Regional Haze SIP with the particularities of Tennessee appearing in the narrative and 

parts of the Appendices. The data in the Appendices was jointly developed by the State of 

Tennessee and the State of North Carolina working in conjunction with VISTAS. 
6 

Figure 1.4-1. Tennessee Class I areas 

In developing this SIP, TDEC-APC has also considered that emission sources outside of 

Tennessee may affect visibility at these Tennessee Class I areas, and that emission 

sources within Tennessee may affect visibility at Class I areas in neighboring states. 

Through VISTAS, the southeastern states have worked together to assess state-by-state 

contributions to visibility impairment in specific class I areas, including those in 

Tennessee and those affected by emissions from Tennessee. This technical work is 

discussed further in Sections 5, 6, and 7 below. Consultations to date between Tennessee 

and other states are summarized in Section 10; consultations are ongoing. 

Prior to VISTAS, the southern states cooperated in a voluntary regional partnership “to 

identify and recommend reasonable measures to remedy existing and prevent future 

adverse effects from human-induced air pollution on the air quality related values of the 

Southern Appalachian Mountains”. States cooperated with FLMs, the USEPA, industry, 

environmental organizations and academia to complete a technical assessment of the 

impacts of acid deposition, ozone, and fine particles on sensitive resources in the 

Southern Appalachians. The (Southern Appalachian Mountain Initiative) SAMI Final 

Report was delivered in August 2002. The SAMI Assessment concluded that ammonium 

sulfate is the major contributor to visibility impairment in the Southern Appalachian 

Mountains and to improve visibility, it is most important to reduce sulfur dioxide 

emissions. SAMI also concluded that reducing ammonia emissions would be helpful to 

reduce ammonium nitrate contributions to visibility impairment. Emissions controls for 

organic carbon, elemental carbon, and soil were expected to be less important for 

improving visibility. The SAMI modeling found that on the haziest days, much of the 

benefit of emissions reductions would occur in the state where emissions reductions were 

made. Emissions in SAMI states and states outside the SAMI region also contribute to 

air quality in the SAMI Class I areas. The SAMI states supported strong national multipollutant legislation to accomplish its mission. Emissions reductions to meet national 

health standards for ozone and fine particles were expected to also improve air quality in 

the Southern Appalachian Mountains. The SAMI states committed to consider air quality 

benefits in the Southern Appalachians as they developed State Implementation Plans for 

the health standards. 

Great Smoky Mountains National Park

Joyce KilmerSlickrock
7 

The Tennessee Valley Authority (TVA) operates several coal-fired electric utility power 

plants in Tennessee. The Tennessee Air Pollution Control Board has been overseeing the 

efforts of the TVA to control both its nitrogen oxide and sulfur dioxide emissions through 

formal updates from its senior management to the Board. The Board has made it clear to 

TVA that it expects the reductions to be significant and targeted to most effectively 

address nonattainment and the protection of Class I areas. If TVA fails to satisfy the 

Board’s expectations, rulemaking will follow. To TVA’s credit, thousands of tons of 

emissions have been or will shortly be reduced in areas where it was needed most. Their 

reductions are also consistent with the SAMI analysis and recommendations. 

Congress considered several legislative bills to reduce sulfur dioxide and nitrogen oxides 

from electric generating utilities. In 2004, USEPA promulgated the Clean Air Interstate 

Rule (CAIR) to require emissions reductions for sulfur dioxide and nitrogen dioxide from 

electric generating utilities in 26 eastern states. The CAIR rule allows for interstate 

trading of emissions to find cost effective reductions. These reductions will improve 

visibility in Class I areas in Tennessee. 

1.5 State and Federal Land Manager (FLM) coordination 

As required by 40 CFR §51.308(i), the regional haze SIP must include procedures for 

continuing consultation between the States and Federal Land Managers on the 

implementation of the visibility protection program, including development and review 

of implementation plan revisions and 5-year progress reports, and on the implementation 

of other programs having the potential to contribute to impairment of visibility in any 

mandatory Class I Federal area within the State. The three FLMs are the United States 

Department of Interior’s (USDI’s) Fish and Wildlife Service (FWS) and National Park 

Service (NPS) and the United States Department of Agriculture’s (USDA’s) Forest 

Service (FS). 

Coordination of Tennessee’s continuing obligations to periodically revise its regional 

haze SIP with the Federal Land Managers is also discussed in Section 11.0 – 

COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN REVISIONS. TDECAPC formally commits to follow the Federal Land Manager consultation procedures as 

prescribed in 40 CFR Section 51.308(i) in making these future plan reviews and 

revisions. 

Pursuant to 40 CFR 51.308(i)(4), procedures are required to be declared that detail the 

coordination with the Federal Land Managers and TDEC-APC with respect to regional 

haze matters. In addition to complying with 40 CFR 51.308(i), TDEC-APC declares that 

it will offer the Federal Land Managers an opportunity to meet on a yearly basis to 

discuss the progress being made to improve visibility and any Federal Land Manager 

suggested improvements to the process that are relevant to the efforts of TDEC-APC to 

comply with the regulations governing the development and implementation of regional 

haze SIPs. The timing of these yearly meetings will be approximately 60 days after the 

IMPROVE monitoring data is finalized for the preceding year. 
8 

The Federal Land Managers were extensively involved in the preparation of this Regional 

Haze SIP. Documentation of the formal comments made by the Federal Land Managers 

and TDEC-APC’s response appears in Appendix N – Public Hearing Comment Summary 

and Agency Responses. 

Successful implementation of a regional haze program will involve long-term regional 

coordination among States. VISTAS was formed in 2001 to address regional haze and 

visibility problems in the southeastern United States. Jurisdictions represented by 

VISTAS members include the Eastern Band of Cherokee Indians; the States of Alabama, 

Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, 

Virginia, and West Virginia; and the local air pollution control programs located in these 

States. A copy of the VISTAS Memorandum of Agreement and Bylaws is enclosed as 

Appendix A. 

The objectives of the VISTAS project are to establish natural background visibility 

conditions across the mandatory Class I Federal areas, identify current visibility 

impairment levels, analyze emission control levels that will achieve interim visibility 

goals, and provide adequate documentation to member agencies so that they can develop 

their regional haze State/Tribal Implementation Plans (SIP/TIP). Figure 1.5-1 shows the 

18 mandatory Class I Federal areas in the VISTAS Region, where visibility is an 

important value. Table 1.5-1 lists these Class I areas. 

Figure 1.5-1. Class I Areas in the VISTAS Region 
9 

Mandatory Class I Federal Areas in the VISTAS Region 

State Area Name Acreage Federal 

Land 

Manager 

Alabama Sipsey Wilderness 24,922 USDA-FS 

Florida 

Chassahowitzka Wilderness 23,579 USDI-FWS 

Everglades National Park 1,397,429 USDI-NPS 

St. Marks Wilderness 17,350 USDI-FWS 

Georgia 

Cohutta Wilderness 36,977 USDA-FS 

Okefenokee Wilderness 353,981 USDI-FWS 

Wolf Island Wilderness 5,126 USDI-FWS 

Kentucky Mammoth Cave National Park 51,303 USDI-NPS 

North Carolina 

Great Smoky Mountains National Park 273,551 USDI-NPS 

Joyce Kilmer-Slickrock Wilderness 13,562 USDA-FS 

Linville Gorge Wilderness 11,786 USDA-FS 

Shining Rock Wilderness 18,483 USDA-FS 

Swanquarter Wilderness 8,785 USDI-FWS 

South Carolina Cape Romain Wilderness 29,000 USDI-FWS 

Tennessee Great Smoky Mountains National Park 241,207 USDI-NPS 

Joyce Kilmer-Slickrock Wilderness 3,832 USDA-FS 

Virginia James River Face Wilderness 8,886 USDA-FS 

Shenandoah National Park 190,535 USDI-NPS 

 West Virginia Dolly Sods Wilderness 10,215 USDA-FS 

Otter Creek Wilderness 20,000 USDA-FS 

Table 1.5-1. Mandatory Class I Federal Areas in the VISTAS Region 

2.0 ASSESSMENT OF BASELINE AND CURRENT CONDITIONS 

AND ESTIMATE OF NATURAL BACKGROUND CONDITIONS IN 

CLASS I AREAS 

The goal of the Regional Haze Rule is to restore natural visibility conditions to the 156 

Class I areas identified in the 1977 Clean Air Act Amendments. 40 CFR 51.301(q) 

defines natural conditions: “Natural conditions include naturally occurring phenomena 

that reduce visibility as measured in terms of light extinction, visual range, contrast, or 

coloration.” The Regional Haze SIPs must contain measures that make “reasonable 

progress” toward this goal by reducing anthropogenic, i.e., manmade, emissions that 

cause haze. 
10 

An easily understood measure of visibility to most people is visual range. Visual range is 

the greatest distance, in kilometers or miles, at which a dark object can be viewed against 

the sky. For evaluating the relative contributions of pollutants to visibility impairment, 

however, the most useful measure of visibility impairment is light extinction, which is 

usually expressed in units of inverse megameters (Mm-1). Light extinction affects the 

clarity and color of objects being viewed. 

The measure used by the regional haze rule is the deciview (dv). Deciviews are 

calculated directly from light extinction using a logarithmic scale. The deciview is a 

useful measure for tracking progress in improving visibility, because each deciview 

change is an equal incremental change in visibility perceived by the human eye. Most 

people can detect a change in visibility at one deciview. 

For each Class I area, there are three metrics of visibility that are part of the 

determination of reasonable progress: 

1) natural conditions, 

2) baseline conditions and 

3) current conditions. 

Each of the three metrics includes the concentration data of the visibility pollutants as 

different terms in the light extinction algorithm, with respective extinction coefficients 

and relative humidity factors. Total light extinction when converted to deciviews (dv) is 

calculated for the average of the 20 percent best and 20 percent worst visibility days. 

“Natural” visibility is determined by estimating the natural concentrations of visibility 

pollutants and then calculating total light extinction. “Baseline” visibility is the starting 

point for the improvement of visibility conditions. It is the average of the Interagency 

Monitoring of Protected Visual Environments (IMPROVE) monitoring data for 2000 

through 2004 and is equivalent to “current” visibility conditions for this initial review 

period. The comparison of initial baseline conditions to natural visibility conditions 

indicates the amount of improvement necessary to attain natural visibility by 2064. Each 

state must estimate natural visibility levels for Class I areas within its borders in 

consultation with Federal Land Managers and other states [40 CFR 51.308(d)(2)]. 

“Current conditions” are assessed every five years as part of the SIP review where actual 

progress in reducing visibility impairment is compared to the reductions committed to in 

the SIP. 

2.1 Estimating Natural Conditions for Tennessee Class I Areas 

Natural background visibility, as defined in Guidance for Estimating Natural Visibility 

Conditions Under the Regional Haze Program, EPA-454/B-03-005, September 2003, is 

based on annual average concentrations of fine particle components. The same annual 

average natural background visibility is assumed for all Class I areas in the eastern 

United States (separate values are estimated for the western United States). Natural 

background visibility for the 20 percent worst days is estimated by assuming that fine 

particle concentrations for natural background are normally distributed and the 90th
11 

percentile of the annual distribution represents natural background visibility on the 20 

percent worst days (i.e, Method 1 prediction per EPA default estimate guidance). For 

detailed information on the 20 percent worst days and 20 percent best days for TN Class I 

areas for the baseline year 2002, please refer to Appendix F-2. 

In the 2003 guidance, USEPA also provided that states may use a “refined approach” to 

estimate the values that characterize the natural visibility conditions of the Class I areas. 

The purpose of such a refinement would be to provide more accurate estimates with 

changes to the extinction algorithm that may include the concentration values, factors to 

calculate extinction from a measured particular species and particle size, the extinction 

coefficients for certain compounds, geographical variation (by altitude) of a fixed value, 

and the addition of visibility pollutants. 

In 2005 the IMPROVE Steering Committee made recommendations for a refined 

equation that modifies the terms of the original equation to account for the most recent 

data. The choice between use of the old or the new equation for calculating the visibility 

metrics for each Class I area is made by the state in which the Class I area is located. 

The new IMPROVE equation accounts for the effect of particle size distribution on light 

extinction efficiency of sulfate, nitrate, and organic carbon. The mass multiplier for 

organic carbon (particulate organic matter) is increased from 1.4 to 1.8. New terms are 

added to the equation to account for light extinction by sea salt and light absorption by 

gaseous nitrogen dioxide. Site-specific values are used for Rayleigh scattering to account 

for the site-specific effects of elevation and temperature. Separate relative humidity 

enhancement factors are used for small and large size distributions of ammonium sulfate 

and ammonium nitrate and for sea salt. The elemental carbon (light-absorbing carbon), 

fine soil, and coarse mass terms do not change between the original and new IMPROVE 

equation. 

Natural background conditions using the new IMPROVE equation are calculated 

separately for each Class I area. The calculation starts with the annual average values for 

natural background for each component of PM2.5 mass from the EPA 2003 guidance 

(default values). The annual frequency distribution of values of each PM2.5 component 

for current conditions (2000-2004) is then defined. This species-specific frequency 

distribution is applied to the default annual average values for that PM2.5 component to 

calculate natural conditions on the 20% worst days. The current variability in each 

component is retained while also retaining the same annual average background 

condition for that component as defined in the 2003 guidance. The new calculation of 

natural background allows Rayleigh scattering to vary with elevation. Current sea salt 

values are used for natural background levels of sea salt. 

The VISTAS states chose to use the new IMPROVE equation as the basis for the 

conceptual description because it takes into account the most recent review of the science 

and because it is recommended by the IMPROVE Steering Committee. For more 

detailed discussion of the two IMPROVE equations, see Appendix B. 
12 

2.2 Estimating Baseline Conditions for Tennessee Class I Areas 

Baseline visibility conditions at each Tennessee Class I area are estimated using sampling 

data collected at the IMPROVE monitoring site at one of the two Class I areas in 

Tennessee. A five year average (2000 to 2004) was calculated for each of the 20 percent 

worst and 20 percent best visibility days in accordance with 40 CFR 51.308(d)(2) and 

Guidance for Tracking Progress Under the Regional Haze Rule, EPA-454-03-004, 

September 2003. IMPROVE data records for Great Smoky Mountains for the period 

2000 to 2004 meet the USEPA requirements for data completeness (75 percent for the 

year and 50 percent for each quarter). IMPROVE does not operate a monitor at Joyce 

Kilmer Wilderness and considers the IMPROVE monitor at Great Smoky Mountains to 

be representative of visibility in the Joyce Kilmer area. The light extinction and 

deciview visibility values for the 20 percent worst and 20 percent best visibility days at 

the Class I areas are based on data and calculations included in Appendix B of this SIP. 

2.3 Summary of Natural Background and Baseline Conditions for Tennessee Class I 

Areas 

Table 2.3-1 presents estimated natural background and baseline visibility metrics for 

Tennessee Class I areas. Note that Tennessee is not considering international emissions to 

be a component of natural background. Baseline visibility on the 20 percent worst days at 

Great Smoky Mountains National Park is approximately 30 dv. Natural background 

visibility at Great Smoky Mountains National Park is predicted to be between 11 and 12 

dv. For detailed information, see Appendix F-2 (MPE for GRSM) and Appendix G 

(Modeling Results & Supplemental Analyses including 2002 PM2.5 species 

concentration data). 

Table 2.3-1 Natural Background and Baseline Conditions for Tennessee Class I 

Areas

Natural Background Conditions 

Class 1 area 

Average for 

20 percent 

Worst Days 

(deciviews) 

Average for 

20 percent 

Best Days 

(deciviews) 

Average for 

20 percent 

Worst Days 

Bext (Mm-1) 

Average for 

20 percent 

Best Days 

Bext (Mm-1) 

Great Smoky Mountains 11.1 4.5 30.8 15.8 

Joyce Kilmer Wilderness 11.1 4.5 30.8 15.8 

Baseline Visibility Conditions 2000-2004 

Class 1 area 

Average for 

20 percent 

Worst Days 

(deciviews) 

Average for 

20 percent 

Best Days 

(deciviews)

Bext (Mm-1) 

Average for 

20 percent 

Worst Days

Bext (Mm-1) 

Average for 

20 percent 

Best Days

Great Smoky Mountains 30.3 13.6 216.3 40.2 

Joyce Kilmer Wilderness 30.3 13.6 216.3 40.2 
13 

2.4 Pollutant Contributions to Visibility Impairment (2000-2004 Baseline Data) 

The 20 percent worst visibility days at Great Smoky Mountains National Park generally 

occur in the period April to September. The peak hazy days occur in the summer under 

stagnant weather conditions with high relative humidity, high temperatures, and low wind 

speeds (see Figure 2.4.1). The 20 percent best visibility days at the Southern 

Appalachian sites can occur at any time of year. See Figures 2.42, and 2.4.3 for visibility 

impairing pollutant attribution on these days by Class I area. 

0

50

100

150

200

250

300

350

3/8 5/17 6/3 6/10 6/28 7/1 7/8 7/22 8/5 8/16 8/19 8/23 8/26 10/15 10/18 10/21 10/24 10/27 11/2 12/11 5/1 5/7 6/9 6/12 6/21 6/27 7/3 7/12 7/18 7/21 7/24 7/30 8/2 8/5 8/8 8/11 8/14 8/17 8/23 8/26 9/7 9/13 9/19 11/18 2/25 5/2 5/8 5/17 6/1 6/13 6/19 6/22 7/1 7/7 7/10 7/16 7/31 8/3 8/6 8/9 8/12 8/15 8/21 9/5 9/8 9/11 9/17 9/23 3/16 4/27 5/24 5/27 6/5 6/11 6/20 6/26 6/29 7/5 7/17 7/20 7/26 8/7 8/10 8/16 8/19 8/22 8/25 8/28 9/3 9/6 9/12 9/21 1/10 2/21 6/8 6/11 6/20 6/26 6/29 7/8 7/17 7/20 7/29 8/4 8/10 8/16 8/19 8/25 8/28 8/31 9/3 9/12 9/15 9/30 10/3 10/30

2000 2001 2002 2003 2004

Extinction (Mm-1)

ECM

ESoil

ELAC

EOMC

EAmm_NO3

EAmm_SO4

Rayleigh

Great Smoky Mtns., TN

2000-2004 Reconstructed Extinction

New IMPROVE Algorithm

20% Worst Days

Figure 2.4-1. Reconstructed Extinction for the 20 percent Haziest Days in 2000-

2004 at Great Smoky Mountains Class I area using New IMPROVE equation 

Ammonium sulfate, (NH4)2SO4, is the most important contributor to visibility impairment 

and fine particle mass on the 20 percent worst and 20 percent best visibility days at both 

Tennessee Class I areas. Sulfate levels on the 20 percent worst days account for 60-70 

percent of the visibility impairment. Across the VISTAS region, sulfate levels are higher 

at the Southern Appalachian sites than at the coastal sites (Figure 2.4-2). On the 20 

percent clearest days, sulfate levels are more uniform across the region (Figure 2.4-3). 

[Note that in these two figures, levels at Great Smoky Mountains National Park should be 

considered to be representative of levels at Joyce Kilmer-Slickrock Wilderness.] Sulfate 

particles are formed in the atmosphere from sulfur dioxide emissions. Sulfate particles 

occur as hydrogen sulfate, H2(SO4), ammonium bisulfate HNH4SO4, and ammonium 

sulfate, (NH4)2SO4, depending on the availability of ammonia, NH3, in the atmosphere. 
14 

Figure 2.4-2. Average light extinction for the 20 percent Haziest Days in 2000-2004 

at VISTAS and neighboring Class I areas using New IMPROVE equation 

Figure 2.4-3. Average light extinction for the 20 percent Clearest Days in 2000-2004 

at VISTAS and neighboring Class I areas using New IMPROVE equation 

VISTAS coastal VISTAS inland Neighboring non-VISTAS

0

50

100

150

200

250

300

Swanquarter, NC

Cape Romain, SCOkefenokee, GA

Everglades, FL

Chassahowitzka, FL

St. Marks, FL

Dolly Sods, WV

Shenandoah, VA

James River Face, VA

Linville Gorge, NC

Shining Rock, NC

Great Smoky Mtns., TN

Cohutta, GA

Sipsey, AL

Mammoth Cave, KY

Brigantine, NJ

Breton, LA

Mingo, MO

Hercules Glades, MO

Upper Buffalo, AR

Caney Creek, AR

Extinction (Mm -1)

Sea Salt

Coarse

Soil

EC

POM

NH4NO3

(NH4)2SO4

Rayleigh

Average Extinction for 20% Haziest Days in 2000-2004

New IMPROVE Algorithm 

0

10

20

30

40

50

60

Extinction (Mm-1)

Sea Salt

CM

Soil

EC

POM

NH4NO3

(NH4)2SO4

Rayleigh

VISTAS coastal VISTAS inland Neighboring non-VISTAS

Swanquarter, NC

Cape Romain, SCOkefenokee, GA

Everglades, FL

Chassahowitzka, FL

St. Marks, FL

Dolly Sods, WV

Shenandoah, VA

James River Face, VA

Linville Gorge, NC

Shining Rock, NC

Great Smoky Mtns., TN

Cohutta, GA

Sipsey, AL

Mammoth Cave, KY

Brigantine, NJ

Breton, LA

Mingo, MO

Hercules Glades, MO

Upper Buffalo, AR

Caney Creek, AR

Average Extinction for 20% Clearest Days in 2000-2004

New IMPROVE Algorithm 
15 

Particulate Organic Matter (POM) is the second most important contributor to fine 

particle mass and light extinction on the 20 percent haziest and the 20 percent clearest 

days at the Tennessee Class I areas. Elevated levels of POM and Elemental Carbon, EC, 

indicate impact from wildfires or prescribed fire. Significant fire impacts are infrequent 

at Class I areas in TN. VISTAS collected additional samples of carbon at five sites, 

including Great Smoky Mountains and Millbrook, (Raleigh, NC), to better understand 

sources contributing to carbon in rural and urban areas. Samples were analyzed to define 

the amount of carbon-14 isotope as an indicator of the amount of carbon from modern 

sources (vegetative emissions, or fires) and the amount of carbon from fossil sources 

(gasoline, diesel or oil). For most samples, the ratio of modern carbon to fossil carbon 

was greater than 0.60 throughout the year. In the fall, winter and spring, more of the 

modern carbon is attributable to wood burning while in the summer months, more of the 

modern carbon mass is attributable to biogenic emissions from vegetation. On some 

days, greater than 90% of the carbon at Great Smoky Mountains is attributable to modern 

sources of carbon. Carbon from gasoline and diesel engines is a relatively small 

contribution at the rural sites. These results suggest that controlling anthropogenic 

sources of carbon will have little benefit in improving visibility in Class I areas since the 

majority of the POM comes from natural, i.e., biogenic sources. Controlling 

anthropogenic sources of carbon will likely be more effective to reduce levels of PM2.5 

in urban areas. 

Ammonium nitrate, NH4NO3, is formed in the atmosphere by reaction of ammonia and 

nitrogen oxides. In the VISTAS region, nitrate formation is limited by availability of 

ammonia and by temperature. Ammonia preferentially reacts with sulfur dioxide and 

sulfate before reacting with nitrogen oxides. Particle nitrate is formed at lower 

temperatures while at elevated temperatures nitric acid remains in gaseous form. For this 

reason, particle nitrate levels are very low in the summer and a minor contributor to 

visibility impairment. Particle nitrate concentrations are higher on winter days and are 

more important for the coastal sites where 20 percent worst days can occur on winter 

days. Nitrogen oxides are emitted by fossil fuel combustion by point, area, on road, and 

non-road sources. Modeling data (see section 7) indicate that in the VISTAS region 

ammonium nitrate formation is limited by ammonia concentrations and suggest that for 

winter days, control of ammonia sources would be more effective in reducing ammonium 

nitrate levels than control of nitrogen oxides. 

Elemental Carbon, EC, is a comparatively minor contributor to visibility impairment. 

Sources include agriculture, prescribed, wildland, and wild fires and incomplete 

combustion of fossil fuels. EC levels are higher at urban monitors than at the Class I 

areas and suggest controls of fossil fuel combustion sources would be more effective to 

reduce PM2.5 in urban areas than to improve visibility in Class I areas. 

Soil fine particles are minor contributors to visibility impairment at most southeastern 

sites on most days. Occasional episodes of elevated fine soil can be attributed to Saharan 

dust episodes, particularly at Everglades, FL, but rarely are seen at the TN Class I areas. 

No control strategies are indicated for fine soil. 
16 

Sea salt, NaCl, is observed at the coastal sites. Sea salt contributions to visibility 

impairment are most important on the 20 percent clearest days when sulfate and POM 

levels are low. Sea salt levels do not contribute significantly to visibility on the 20 

percent worst visibility days. The new IMPROVE equation uses Chloride ion, Cl-

, from 

routine IMPROVE measurements to calculate sea salt levels. Cl- may react with nitrate and 

volatize off the filter, so that Cl- may underestimate sea salt levels. VISTAS used Cl- to 

calculate sea salt contributions to visibility following IMPROVE guidance. 

Coarse particle mass (particles with diameters between 2.5 and 10 microns) has a 

relatively small contribution to visibility impairment because the light extinction 

efficiency of coarse mass is very low compared to the extinction efficiency for sulfate, 

nitrate, and carbon. 

An unidentified component is reported by IMPROVE as the difference between the total 

PM2.5 mass measured on the filter and the sum of the measured components. This 

unidentified mass may be positive or negative and is attributable to water and/or the 

factors used to calculate molecular weights of the other components. 

 

The New IMPROVE equation generally results in higher calculated light extinction on 

days with higher mass and lower light extinction on days with lower mass. This tends to 

increase calculated light extinction for current conditions and to decrease calculated light 

extinction for natural visibility conditions. Adding sea salt to the new IMPROVE 

equation increases light extinction for both current and natural visibility conditions. 

Increasing the mass multiplier for particulate organic matter in the new IMPROVE 

equation increases light extinction for current conditions more than for natural conditions. 

The new algorithm does not change the conclusion that in the VISTAS region and in 

Tennessee, the most effective means to improve visibility is to reduce sulfate 

concentrations. 

PM2.5 trends in urban and Class I areas: IMPROVE data were compared to monitoring 

data from the Speciated Trends Network (STN) in nearby urban areas to understand the 

similarities and differences in composition of fine particle mass. Several PM2.5 

nonattainment areas are in close proximity to the Class I areas in the southeastern United 

States, including Atlanta, GA; Birmingham, AL; Charleston, WV; Chattanooga, TN; 

Louisville, KY; and Knoxville, TN. Ammonium sulfate concentrations are comparable 

between urban and nearby Class I areas, while organic carbon, elemental carbon, and 

nitrate concentration are generally higher in urban areas than the Class I areas. These 

results suggest that sulfate is widely distributed regionally while urban areas see 

additional incremental pollutant loadings from local emissions sources. 

Role of meteorology in determining visibility conditions: Classification and Regression 

Tree (CART) Analyses were used to characterize the relationship between meteorological 

conditions and visibility conditions at the Class I areas. Days were assigned to one of 

five visibility classes ranging from poor to good visibility. Days were then assigned to 

bins based on meteorological conditions. For the Tennessee Class I areas poor visibility 

days were most likely to occur on days with high temperatures, high relative humidity, 
17 

low wind speeds, and elevated PM2.5 mass at upwind urban areas. Precipitation was not 

a good predictor of visibility condition. Weights were assigned to days based on 

frequency of occurrence of days with similar meteorological conditions. 

The above analyses are further discussed in Appendix B. 

3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064 

As stated in section 1.3 above, the regional haze rule directs states to graphically show 

what would be a “uniform rate of progress” toward natural conditions for each mandatory 

Class I Federal area within the State as well as for each mandatory Class I Federal area 

located outside the State, which may be affected by emissions from sources within the 

State. The uniform rate of progress is also known as the “glidepath.” The glidepath is 

simply a straight graphical line drawn from the baseline level of visibility impairment for 

2000-2004 to the level representing no manmade impairment in 2064. 

Each state must set goals for each Class I area that provide for reasonable progress 

towards achieving natural visibility conditions by 2064. Section 51.308(d)(1) of the 

regional haze rule requires that reasonable progress goals must both: 

(1) provide for improvement in visibility for the most impaired days over the period 

of the implementation plan; and 

(2) ensure no degradation in visibility for the least impaired days over the same 

period. 

Uniform rate of progress graphs (glidepaths), were developed for each Class I area in the 

VISTAS region. The glidepaths were developed in accordance with the USEPA 

guidance for tracking progress and used data collected from the IMPROVE monitoring 

sites as described in section 2 of this document. The glidepath is one of the indicators 

used in setting reasonable progress goals. 

3.1 Glidepaths for Class I Areas in Tennessee 

The following are glidepaths for the 20 percent most impaired days for Great Smoky 

Mountains National Park and Joyce Kilmer-Slickrock Wilderness Area, assuming 

uniform rate of progress toward regional haze goals. Natural background visibility at 

both sites is predicted to be between 11 and 12 dv. Natural background visibility 

calculations are based on EPA’s default natural conditions estimates (Method 1) as 

explained in Appendix B-4 (page 31). 
18 

(Method 1 Prediction = Default Calculation per EPA guidance) 

Figure 3.1-1. Uniform Rate of Progress Glidepath for 20 percent worst days at 

Great Smoky Mountains National Park 

(Method 1 Prediction = Default Calculation per EPA guidance) 

Figure 3.1-2. Uniform Rate of Progress Glidepath for 20 percent worst days at 

Joyce Kilmer Wilderness Area. 

Uniform Rate of Reasonable Progress Glide Path

Great Smoky Mountains - W20% Data Days

30.28 28.67

25.79

22.59

19.38

16.18

12.97

11.05

26.72

23.50

0

5

10

15

20

25

30

35

200

0

200

4

200

8

201

2

201

6

202

0

202

4

202

8

203

2

203

6

204

0

204

4

204

8

205

2

205

6

206

0

206

4

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide Path

Joyce Kilmer-Slickrock - W20% Data Days

30.28

28.67

25.79

22.59

19.38

16.18

12.97

11.05

26.72

23.50

0

5

10

15

20

25

30

35

200

0

200

4

200

8

201

2

201

6

202

0

202

4

202

8

203

2

203

6

204

0

204

4

204

8

205

2

205

6

206

0

206

4

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction
19 

4.0 TYPES OF EMISSIONS IMPACTING VISIBILITY IMPAIRMENT 

IN TENNESSEE CLASS I AREAS

4.1 Baseline Emissions Inventory 

The Regional Haze Rule at 51.308(d) (4) (v) requires a statewide emissions inventory of 

pollutants that are reasonably anticipated to cause or contribute to visibility impairment in 

any mandatory Class I area. An inventory was developed for the baseline year 2002 and 

projected to 2009 and 2018. The pollutants inventoried include volatile organic 

compounds, nitrogen oxides, fine particulate (PM2.5), coarse particulate (PM10), 

ammonia and sulfur dioxide. The baseline emissions inventory for 2002 was developed 

for Tennessee following the methods described in Appendix D. 

There are five different emission inventory source classifications: stationary point and 

area sources, off-road and on-road mobile sources, and biogenic sources. Stationary 

point sources are those sources that emit greater than a specified tonnage per year, with 

data provided at the facility level. Electric generating utilities and industrial sources are 

the major categories for stationary point sources. Stationary area sources are those 

sources whose individual emissions are relatively small, but due to the large number of 

these sources, the collective emissions from the source category could be significant (i.e., 

dry cleaners, service stations, agricultural sources, fire emissions, etc.). These types of 

emissions are estimated on a countywide level. Non-road (or off-road) mobile sources 

are equipment that can move but do not use the roadways, i.e., lawn mowers, 

construction equipment, railroad locomotives, aircraft, etc. The emissions from these 

sources, like stationary area sources, are estimated on a countywide level. On-road 

mobile sources are automobiles, trucks, and motorcycles that use the roadway system. 

The emissions from these sources are estimated by vehicle type and road type and are 

summed to the countywide level. Biogenic sources are the natural sources like trees, 

crops, grasses and natural decay of plants. The emissions from these sources are 

estimated on a countywide level. 

In addition to the various source classifications, there are also various types of emission 

inventories. The first is the actual base year inventory. This inventory is the base year 

emissions that correspond to the meteorological data used, which for this modeling effort 

is data from 2002. These emissions are used for evaluating the air quality model 

performance. 

The second type of inventory is the typical base year inventory. This inventory is similar 

to the actual base year inventory, except that for sources whose emissions change 

significantly from year to year, a more typical emission value is used. In this modeling 

effort, typical emissions were developed for the electric generating units (EGUs) and the 

wildland fire emissions. The air quality modeling is run using the typical base year 

inventory and the future year inventory. The results from these two runs are then used to 

calculate relative reduction factors. These relative reduction factors for future years are 

then used to demonstrate reasonable progress toward visibility goals. 
20 

Below is an overview of the inventories used for each source classification. More 

detailed discussion of the emissions inventory development is contained in Appendix D. 

4.1.1 Stationary Point Sources 

Point source emissions are emissions from individual sources having a fixed location. 

Generally, these sources must have permits to operate, and their emissions are 

inventoried on a regular schedule. Large sources emitting at least 100 tons per year (tpy) 

of a criteria pollutant, 10 tpy of a single hazardous air pollutant (HAP), or 25 tpy total 

HAP are inventoried annually. Smaller sources have been inventoried less frequently. 

The point source emissions data can be grouped as EGU sources and other industrial 

point sources, also called non-EGUs. 

Electric Generating Units

The actual base year inventory for the EGU sources used 2002 continuous emissions 

monitoring (CEM) data reported to the USEPA’s Acid Rain program or 2002 hourly 

emissions data provided by stakeholders. These data provide hourly emissions profiles 

for SO2 and NOx that can be used in air quality modeling. Emissions profiles are used to 

estimate emissions of other pollutants (volatile organic compounds, carbon monoxide, 

ammonia, fine particles, soil) based on measured emissions of SO2 and NOx. 

Emissions from EGUs vary daily and seasonally as a function of variability in energy 

demand and utilization and outage schedules. To avoid anomalies in future year 

emissions created by relying on 2002 operations to represent future operations, a typical 

base year emissions inventory was developed for EGUs. This approach is consistent with 

the USEPA’s 2007 modeling guidance. To develop a typical year 2002 emissions 

inventory for EGU sources, each unit’s average CEM heat input for 2000 through 2004 

was divided by the 2002 actual heat input to generate a unit specific normalizing factor. 

This normalizing factor was then multiplied by the 2002 actual emissions. The heat 

inputs for the period 2000 through 2004 were used because the modeling current 

conditions use monitored data from this same 5-year period. If a unit was shut down for 

an entire year during the 2000 through 2004 period, the average of the years the unit was 

operational was used. If a unit was shut down in 2002, but not permanently shutdown, 

the emissions and heat inputs from 2001 (or 2000) were used in the normalizing 

calculations. 

As part of the VISTAS air quality modeling, VISTAS, in cooperation with the other 

eastern RPOs, contracted with ICF Resources, L.L.C., to generate future year emission 

inventories for the electric generating sector of the contiguous United States using the 

Integrated Planning Model (IPM) version 2.1.9 updated with state specific information. 

IPM is a dynamic linear optimization model that can be used to examine air pollution 

control policies for various pollutants throughout the contiguous United States for the 

entire electric power system. The dynamic nature of IPM enables projection of the 

behavior of the power system over a specified future period. Optimization logic in IPM 

determines the least-cost means of meeting electric generation and capacity requirements 
21 

while complying with specified constraints including air pollution regulations, 

transmission bottlenecks, and plant-specific operational constraints. The versatility of 

IPM allows users to specify which constraints to exercise, and to populate IPM with their 

own datasets. TDEC-APC worked closely with the TVA in terms of an IPM “reality 

check” to gain a more accurate understanding of its plans for future generation capacity 

and air pollution controls. 

 

Other Industrial Point Sources

For the non-EGU sources, the same inventory is used for both the actual and typical base 

year emissions inventories. The non-EGU category uses annual emissions as reported 

under the Consolidated Emissions Reporting Rule (CERR) for the year 2002. These 

emissions are temporally allocated to month, day, and hour using source category code 

(SCC)-based allocation factors. 

The general approach for assembling future year data was to use recently updated growth 

and control data consistent with USEPA’s CAIR analyses. This data was supplemented 

with state-specific growth factors and stakeholder input on growth assumptions. 

4.1.2 Stationary Area Sources 

Stationary area sources are sources whose individual emissions are relatively small, but 

due to the large number of these sources, the collective emissions could be significant 

(i.e., combustion of fuels for heating, structure fires, service stations, etc.). Emissions are 

estimated by multiplying an emission factor by some known indicator of collective 

activity, such as fuel usage, number of households, or population. Stationary area source 

emissions are estimated at the countywide level. 

A portion of the area source 2002 base year inventory for Tennessee was developed by 

TDEC-APC in conjunction with its contractor, the University of Tennessee at Knoxville, 

and provided to the VISTAS contractor. The VISTAS contractor calculated the 

remaining portion of the area source inventory. The sources estimated by the contractor 

include emissions from animal husbandry, wildland fires, and particulate matter from 

paved and unpaved roads. For the other states within the modeling domain, either statesupplied data or data reported under CERR for 2002 was used. 

The actual base year inventory will serve as the typical base year inventory for all area 

source categories except for wildland fires. For wildland fires, a typical year inventory 

was used to avoid anomalies in wildfire activity in 2002 compared to longer-term 

averages. Development of a typical year WILDfire inventory provided the capability of 

using a comparable data set for both the base year and future years. Thus, fire emissions 

remain the same for air quality modeling in both the base and any future years. The 

VISTAS Fire Special Interest Work Group used State records to ratio the number of acres 

burned over a longer term period (three or more years, as available from state records) to 

2002. Based on these ratios, the 2002 acreage was then scaled up or down to develop a 

typical year inventory. 
22 

The VISTAS contractor generated future year emissions inventories for 2009 and 2018 

for the regional haze modeling. Growth factors, supplied either by states or taken from 

the CAIR emission projections, were applied to project the controlled emissions. If no 

growth factor was available from either a state or the CAIR growth factor files, then the 

USEPA’s Economic Growth and Analysis System Version 5 growth factors were used. 

4.1.3 Off-Road Mobile Sources 

Off-road mobile sources are equipment that can move but do not use the roadways, such 

as construction equipment, aircraft, railroad locomotives, lawn and garden equipment, 

etc. For the majority of the off-road mobile sources, the emissions for 2002 were 

estimated using the USEPA’s NONROAD2005c model. For the three source categories 

not included in the NONROAD model, i.e., aircraft engines, railroad locomotives and 

commercial marine, more traditional methods of estimating the emissions were used. 

The same inventory is used for both the actual and typical base year emissions 

inventories. 

For the source categories estimated using the USEPA’s NONROAD model, the model 

growth assumptions were used to create the 2009 and 2018 future year inventories. The 

NONROAD model takes into consideration regulations affecting emissions from these 

source categories. For the commercial marine, railroad locomotives and airport 

emissions, the VISTAS contractor calculated the future growth in emissions using 

detailed inventory data (both before and after controls) for 1996 and 2010, obtained from 

the CAIR Technical Support Document. When available, state-specific growth factors 

were used. 

4.1.4 Highway Mobile Sources 

For on-road vehicles, the newest version of the MOBILE model, MOBILE6.2, was used. 

Key inputs for MOBILE include information on the age of vehicles on the roads, the 

average speeds on the roads, the mix of vehicles on the roads, any programs in place in 

an area to reduce emissions for motor vehicles (e.g., emissions inspection programs), and 

temperature. 

The MOBILE model takes into consideration regulations that affect emissions from this 

source sector. The same MOBILE run is used to represent the actual and typical year 

emissions for on-road vehicles using input data reflective of 2002. The MOBILE model 

then is run for 2018 inventory using input data reflective of that year. 

4.1.5 Biogenic Emission Sources 

Biogenic emissions were prepared with the SMOKE-BEIS3 (Biogenic Emission 

Inventory System 3 version 0.9) preprocessor. SMOKE-BEIS3 is a modified version of 

the Urban Airshed Model (UAM)-BEIS3 model. Modifications include use of MM5 

data, gridded land use data, and improved emissions characterization. The emission 

factors that are used in SMOKE-BEIS3 are the same as the emission factors as in UAM-
23 

BEIS3. The basis for the gridded land use data used by BEIS3 is the county land use data 

in the Biogenic Emissions Landcover Database version 3 (BELD3) provided by the 

USEPA. A separate land classification scheme, based upon satellite (AVHRR, 1 km 

spatial resolution) and census information, aided in defining the forest, agriculture and 

urban portions of each county. 

4.1.6 Summary 2002 Base G4 (March 2008 version) Best and Final Baseline 

Emissions Inventory for Tennessee 

Table 4.1.6-1 is a summary of the 2002 baseline actual Base G4 (Best and Final) 

emission inventory for Tennessee. Table 4.1.6-2 is a summary of the 2002 baseline 

typical emission inventory. The complete inventory and discussion of the methodology is 

contained in Appendix D. The emissions summaries for other VISTAS states can also be 

found in Appendix D. 

Table 4.1.6-1 2002 Actual Base G4 (B&F) Emissions Inventory Summary for TN in 

tons per year. 

Source Category VOC NOx PM2.5 PM10 NH3 SO2 

Point 85,254 221,651 39,973 49,814 1,817 413,755 

Area 153,509 17,936 42,925 212,972 34,412 29,942 

On-Road Mobile 179,807 238,577 3,949 5,371 6,625 9,226 

Non-Road Mobile 66,450 96,827 6,458 6,819 43 10,441 

Biogenics 894,214 18,081 0 0 0 0 

TOTAL 1,379,234 593,072 93,305 274,976 42,897 463,364 

Table 4.1.6-2 2002 Typical Base G4 (B&F) Emissions Inventory Summary for TN in 

tons per year. 

Source Category VOC NOx PM2.5 PM10 NH3 SO2 

Point 85,218 216,481 39,298 49,040 1,810 399,750 

Area 153,783 18,061 43,410 213,538 34,439 29,977 

On-Road Mobile 179,807 238,577 3,949 5,371 6,625 9,226 

Non-Road Mobile 66,450 96,827 6,458 6,819 43 10,441 

Biogenics 894,214 18,081 0 0 0 0 

TOTAL 1,379,472 588,027 93,115 274,768 42,917 449,394 

4.1.7 Model Performance Improvements through Emissions Inventory 

Improvements 

Since the initial model performance evaluation, VISTAS has made several improvements 

to the emissions inventory, which in turn improve model performance. These inventory 

improvements are detailed in the VISTAS emissions inventory report and Appendix D, 

and are summarized here: 

• For electric generating utilities, the Integrated Planning Model (IPM) was used to 

provide estimates of future year utility production and emissions. Continuous 

Emissions Monitoring data was used to define seasonal variability in production 
24 

and emissions. The states updated IPM model projections with control data 

provided by utility companies in late 2006 through winter 2007. 

• For on-road vehicle emissions, states and local agencies provided updated 

MOBILE model input and vehicle-miles-traveled data. 

• For ammonia emissions from agricultural sources, the Carnegie Mellon 

University ammonia model was used to improve annual and monthly estimates. 

• For fires, the VISTAS states provided fire activity data for 2002 for wildfires, 

prescribed fire, land clearing and agricultural burning to develop a 2002 fire 

inventory. Where data allowed, large fire events were modeled as point sources. 

In 2006, the United States Forest Service and Fish and Wildlife Service provided 

projections of increased prescribed burning in 2009 and 2018; these data were 

incorporated for all states except Florida. Because current prescribed fire activity 

already reflects the use of fire as a forest management technique, Florida believed 

that there is too much uncertainty to project how future total fire activity 

(prescribed plus wildfire) will change. In Florida, prescribed fire in the future 

years is the same as 2002 typical for prescribed fire. 

• For non-road engines, the updated USEPA NONROAD2005 emissions model 

was used. 

• For commercial marine emissions in shipping lanes in the Gulf of Mexico and 

Atlantic Oceans, gridded emissions for the VISTAS modeling domain were 

created using inventory data newly developed for USEPA by Corbett at 

University of Delaware. These emissions were incorporated in the modeling. 

• Updated inventories from the neighboring RPOs, Mexico, and Canada were 

incorporated as available. 

4.2 Assessment of Relative Contributions from Specific Pollutants and Sources 

Categories 

Ammonium sulfate is the largest contributor to visibility impairment at the Tennessee 

Class I areas, and reduction of SO2 emissions would be the most effective means of 

reducing ammonium sulfate. As illustrated in Figure 4.2-1, 96 percent of SO2 emissions 

VISTAS States SO2 Emissions - 2002 

Utilities

Industrial

On road

Non road

Other

Industrial: 20% Utilities: 76%

4.8 Million tons

VISTAS States SO2 Emissions - 2002 

Utilities

Industrial

On road

Non road

Other

Industrial: 20% Utilities: 76%

4.8 Million tons
25 

in the VISTAS states are attributable to electric generating facilities and industrial point 

sources. Similarly, in Tennessee, the stationary point and area sources consisting of 

electric generating facilities and industrial point sources, contribute 95.7 percent of SO2 

emissions in the state (see Table 4.2-1). 

Table 4.2-1 2002 SO2 Emissions for Tennessee (tons/year) 

Source Category SO2 Emissions Percent 

Point 399,730 89.0 %

Area 29,977 6.7 %

On-Road Mobile 9,127 2.0 %

Off-Road Mobile 10,441 2.3 %

Biogenics 0 0 %

 TOTAL 449,275 100 %

Figure 4.2-1. SO2 emissions in 2002 in the VISTAS States. 

5.0 REGIONAL HAZE MODELING METHODS AND INPUTS 

Modeling for regional haze was performed by VISTAS for the ten southeastern states, 

including Tennessee. The sections below outline the methods and inputs used by 

VISTAS for the regional modeling. Additional details are provided in Appendices C, D 

and M. 

5.1 Analysis Method 

The modeling analysis is a complex technical evaluation that begins by selection of the 

modeling system. VISTAS decided to use the following modeling system: 

• Meteorological Model: The Pennsylvania State University/National Center for 

Atmospheric Research (PSU/NCAR) Mesoscale Meteorological Model (MM5) is 

a nonhydrostatic, prognostic meteorological model routinely used for urban- and 

regional-scale photochemical, fine particulate matter, and regional haze regulatory 

modeling studies. 

• Emissions Model: The Sparse Matrix Operator Kernel Emissions (SMOKE) 

modeling system is an emissions modeling system that generates hourly gridded 

speciated emission inputs of mobile, nonroad mobile, area, point, fire and 

biogenic emission sources for photochemical grid models. 

• Air Quality Model: USEPA’s Models-3/ Community Multiscale Air Quality 

(CMAQ) modeling system is an ‘One-Atmosphere’ photochemical grid model 

capable of addressing ozone, particulate matter (PM), visibility and acid 

deposition at regional scale. 

The USEPA’s 2007 Modeling Guidance recommends modeling an entire year or at a 

minimum several days in each quarter of a year to adequately represent the range of 

meteorological conditions that contribute to elevated levels of fine particulate matter. 
26 

The year 2002 was selected by VISTAS as the modeling year for this demonstration. 

Meteorological inputs were developed for 2002 using the meteorological model. 

Emission inventories were also developed for 2002 and processed through the emissions 

model. These inputs were used in the air quality model to predict fine particle mass and 

visibility. The model results for 2002 were compared with observed meteorological and 

air quality data to evaluate model performance. Several configurations of the 

meteorological and air quality model were evaluated to select a configuration that gave 

the best overall performance for the VISTAS region. 

Once model performance was deemed adequate, the current and future year emissions 

were processed through the emissions model. The air quality modeling results are used 

to determine a relative reduction in future visibility impairment, which is used to 

determine reasonable progress. 

The complete modeling protocol used for this analysis can be found in Appendix C. 

5.2 Model Selection 

To ensure that a modeling study is defensible, care must be taken in the selection of the 

models to be used. The models selected must be scientifically appropriate for the 

intended application and be freely accessible to all stakeholders. Scientifically 

appropriate means that the models address important physical and chemical phenomena 

in sufficient detail, using peer-reviewed methods. Freely accessible means that model 

formulations and coding are freely available for review and that the models are available 

to stakeholders, and their consultants, for execution and verification at no or low cost. 

The following sections outline the criteria for selecting a modeling system that is both 

defensible and capable of meeting the study's goals. These criteria were used in selecting 

the modeling system used for this modeling demonstration. 

5.2.1 Selection of Photochemical Grid Model 

Criteria

For a photochemical grid model to qualify as a candidate for use in a regional haze SIP, a 

State needs to show that it meets the same several general criteria as a model for an 

attainment demonstration for a national ambient air quality standard (NAAQS): 

• The model has received a scientific peer review 

• The model can be demonstrated applicable to the problem on a theoretical basis 

• Data bases needed to perform the analysis are available and adequate 

• Available past appropriate performance evaluations have shown the model is not 

biased toward underestimates or overestimates 
27 

• A protocol on methods and procedures to be followed has been established 

• The developer of the model must be willing to make the source code available to 

users for free or for a reasonable cost, and the model cannot otherwise be 

proprietary. 

Overview of CMAQ

The photochemical model selected for this study was CMAQ version 4.5. For more than 

a decade, the USEPA has been developing the Models-3 CMAQ modeling system with 

the overarching aim of producing a ‘One-Atmosphere’ air quality modeling system 

capable of addressing ozone, fine particulate matter, visibility and acid deposition within 

a common platform. The original justification for the Models-3 development emerged 

from the challenges posed by the 1990 CAAA and the USEPA’s desire to develop an 

advanced modeling framework for ‘holistic’ environmental modeling utilizing state-ofscience representations of atmospheric processes in a high performance computing 

environment. The USEPA completed the initial stage of development with Models-3 and 

released the CMAQ model in mid-1999 as the initial operating science model under the 

Models-3 framework. The most recent rendition is CMAQ version 4.5, which was 

released in September 2005. 

An advantage of choosing CMAQ as the atmospheric model is the ability to do oneatmospheric modeling. The same model configuration is being applied for the ozone and 

PM2.5 attainment demonstrations SIPs, as well as the regional haze SIP. A number of 

features in CMAQ’s theoretical formulation and technical implementation make the 

model well suited for annual PM modeling. 

The configuration used for this modeling demonstration, as well as a more detailed 

description of the CMAQ model, can be found in the Modeling Protocol (Appendix C). 

5.2.2 Selection of Meteorological Model 

Criteria

Meteorological models, either through objective, diagnostic, or prognostic analysis, 

extend available information about the state of the atmosphere to the grid upon which 

photochemical grid modeling is to be carried out. The criteria for selecting a 

meteorological model are based on both the model’s ability to accurately replicate 

important meteorological phenomena in the region of study, and the model's ability to 

interface with the rest of the modeling systems -- particularly the photochemical grid 

model. With these issues in mind, the following criteria were established for the 

meteorological model to be used in this study: 

• Non-Hydrostatic Formulation 

• Reasonably current, peer reviewed formulation 
28 

• Simulates Cloud Physics 

• Publicly available at no or low cost 

• Output available in I/O API format 

• Supports Four Dimensional Data Assimilation (FDDA) 

• Enhanced treatment of Planetary Boundary Layer heights for AQ modeling 

Overview of MM5

The non-hydrostatic MM5 model is a three-dimensional, limited-area, primitive equation, 

prognostic model that has been used widely in regional air quality model applications. 

The basic model has been under continuous development, improvement, testing and open 

peer-review for more than 20 years and has been used worldwide by hundreds of 

scientists for a variety of mesoscale studies. 

MM5 uses a terrain-following non-dimensionalized pressure, or "sigma", vertical 

coordinate similar to that used in many operational and research models. In the nonhydrostatic MM5, the sigma levels are defined according to the initial hydrostaticallybalanced reference state so that the sigma levels are also time-invariant. The gridded 

meteorological fields produced by MM5 are directly compatible with the input 

requirements of ‘one atmosphere’ air-quality models using this coordinate. MM5 fields 

can be easily used in other regional air quality models with different coordinate systems 

by performing a vertical interpolation, followed by a mass-conservation re-adjustment. 

Distinct planetary boundary layer parameterizations are available for air-quality 

applications, both of which represent sub-grid-scale turbulent fluxes of heat, moisture and 

momentum. One scheme uses a first-order eddy diffusivity formulation for stable and 

neutral environments and a modified first-order scheme for unstable regimes. The other 

scheme uses a prognostic equation for the second-order turbulent kinetic energy, while 

diagnosing the other key boundary layer terms. 

Initial and lateral boundary conditions are specified for real-data cases from mesoscale 

three-dimensional analyses performed at 12-hour intervals on the outermost grid mesh 

selected by the user. Surface fields are analyzed at three-hour intervals. A Cressmanbased technique is used to analyze standard surface and radiosonde observations, using 

the National Meteorological Center's spectral analysis, as a first guess. The lateral 

boundary data are introduced using a relaxation technique applied in the outermost five 

rows and columns of the coarsest grid domain. 

MM5 modeling system in regulatory air quality application studies have been widely 

reported in the literature (e.g., Emery et al., 1999; Tesche et al., 2000, 2003) and many 

have involved comparisons with other prognostic models such as the Regional 

Atmospheric Modeling System (RAMS) and the Systems Application International 

Mesoscale Model. The MM5 enjoys a far richer application history in regulatory 

modeling studies compared with RAMS or other models. Furthermore, in evaluations of 
29 

these models in over 60 recent regional scale air quality application studies since 1995, it 

has generally been found that the MM5 model tends to produce somewhat better 

photochemical model inputs than alternative models. 

The configuration used for this modeling demonstration, as well as a more detailed 

description of the MM5 model, can be found in the meteorological modeling protocol 

(Appendix E). 

5.2.3 Selection of Emissions Processing System 

Criteria

The principal criterion for an emissions processing system is that it accurately prepares 

emissions files in a format suitable for the photochemical grid model being used. The 

following list includes clarification of this criterion and additional desirable criteria for 

effective use of the system. 

• File System Compatibility with the I/O API 

• File Portability 

• Ability to grid emissions on a Lambert Conformal projection 

• Report Capability 

• Graphical Analysis Capability 

• MOBILE6 Mobile Source Emissions 

• Biogenic Emissions Inventory System version 3 (BEIS-3) 

• Ability to process emissions for the proposed domain in a reasonable amount of 

time. 

• Ability to process control strategies 

• No or low cost for acquisition and maintenance 

• Expandable to support other species and mechanisms 

Overview of SMOKE

The SMOKE Emissions Processing System Prototype was originally developed at the 

Micro-computing Center of North Carolina. As with most ‘emissions models’, SMOKE 

is principally an emission processing system and not a true emissions modeling system in 

which emissions estimates are simulated from ‘first principles’. This means that, with the 

exception of mobile and biogenic sources, its purpose is to provide an efficient, modern 

tool for converting emissions inventory data into the formatted emission files required by 

an air quality simulation model. For mobile sources, SMOKE actually estimates 

emissions rates based on input mobile-source activity data, emission factors and outputs 

from transportation travel-demand models. 

 
30 

SMOKE was originally designed to allow emissions data processing methods to utilize 

emergent high-performance-computing as applied to sparse-matrix algorithms. Indeed, 

SMOKE is the fastest emissions processing tool currently available to the air quality 

modeling community. The sparse matrix approach utilized throughout SMOKE permits 

both rapid and flexible processing of emissions data. The processing is rapid because 

SMOKE utilizes a series of matrix calculations instead of less efficient algorithms used in 

previous systems. The processing is flexible because the processing steps of temporal 

projection, controls, chemical speciation, temporal allocation, and spatial allocation have 

been separated into independent operations wherever possible. The results from these 

steps are merged together at a final stage of processing. 

 

SMOKE contains a number of major features that make it an attractive component of the 

modeling system. The model supports a variety of input formats from other emissions 

processing systems and models. It supports both gridded and county total land use 

scheme for biogenic emissions modeling. SMOKE can accommodate emissions files 

from up to 10 countries and any pollutant can be processed by the system. 

For additional information about the SMOKE model please refer to Modeling Protocol 

(Appendix C). 

5.3 Selection of the Modeling Year 

A crucial step to SIP modeling is the selection of the period of time to model to represent 

current air quality conditions and to project changes in air quality in response to changes 

in emissions. The year 2002 was selected as the base year for several reasons. 

The USEPA’s April 2007 Guidance on the use of Models and Other Analyses for 

Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze

identifies specific goals to consider when selecting one or more episodes for use in 

demonstrating reasonable progress in attaining the regional haze air quality goals. The 

USEPA recommends that episode selection derive from three principal criteria: 

• Simulate a variety of meteorological conditions; 

• Model time periods in which observed concentrations are close to the appropriate 

baseline design value or visibility impairment; 

• Model periods for which extensive air quality/meteorological data bases exist; and 

• Model a sufficient number of days so that the modeled attainment test applied at each 

monitor violating the NAAQS is based on multiple days. 

For regional haze modeling, the guidance goes further by suggesting that the preferred 

approach is to model a full, representative year. Moreover, the required relative 

reduction factor values should be based on model results averaged over the 20% worst 

and 20% best visibility days determined for each Class I are based on monitoring data 

from the 2000 – 2004 baseline period. 
31 

The USEPA also lists several other considerations to bear in mind when choosing 

potential regional haze episodes including: (a) choose periods which have already been 

modeled, (b) choose periods which are drawn from the years upon which the current 

design values are based, (c) include weekend days among those chosen, and (d) choose 

modeling periods that meet as many episode selection criteria as possible in the 

maximum number of nonattainment or Class I areas as possible. Finally, the USEPA 

explicitly recommended in its 2007 guidance to use 2002 as the baseline inventory year. 

VISTAS adopted a logical, stepwise approach in implementing the USEPA 2007 

modeling guidance in order to identify the most preferable, representative year for 

regional haze modeling. 

These steps include the following: 

Representativeness of Meteorological Conditions: The VISTAS meteorological 

contractor (BAMS) identified important meteorological characteristics and data 

sets in the VISTAS region directly relevant to the evaluation of candidate annual 

modeling episodes. 

Initial Episode Typing: At the time of selection in 2003, meteorological and air 

quality data were available for 2002 for model inputs and model performance 

evaluation. VISTAS used CART Analyses to evaluate visibility conditions for 

2000, 2001, and 2002, the candidate modeling years. The year 2002 was found to 

be representative of conditions in the other two years. Subsequently, these 

analyses were repeated with the meteorological and air quality monitoring data 

for 2000 to 2004 to evaluate how well the 2002 modeling year represented the full 

2000-2004 baseline period. This analysis confirmed that visibility and PM2.5 

mass in 2002 were representative of the five-year baseline period for the VISTAS 

Class I areas. This analysis is discussed in more detail in the project report in 

Appendix B. 

Data Availability: In parallel with the CART analysis, episode characterization 

analyses, collaborative investigations by VISTAS states (e.g., North Carolina 

Division of Air Quality, Georgia Department of Natural Resources, Florida 

Department of Environmental Protection) intensively studied the availability of 

PM2.5, meteorological, and emissions data and representativeness of alternative 

baseline modeling periods from a regulatory standpoint. Additionally, 2002 was 

the year that the USEPA was requiring states to provide emissions inventory data 

for the Comprehensive Emissions Reporting Rule. It made sense to use 2002 as 

the modeling year to take advantage of the 2002 inventory. 

Years to be used by other RPOs: VISTAS also considered what years other RPOs 

would be modeling, and several had already chosen calendar year 2002 as the 

modeling year. 

After a lengthy process of integrated studies, the episode selection process culminated in 

the selection of calendar year 2002 (1 January through 31 December) as the most current, 

representative, and pragmatic choice for VISTAS regional haze modeling. All of the 

USEPA criteria for regional haze episode selection were directly considered in this 
32 

process together with many other considerations (e.g., timing of new emissions or 

aerometric data deliveries by the USEPA or the states to the modeling teams). 

5.4 Modeling Domains 

5.4.1 Horizontal Modeling Domain 

The USEPA’s 2007 modeling guidance recommends a 12-km modeling grid resolution 

for PM2.5 modeling while a 36-km grid is considered acceptable for regional haze. For 

the VISTAS modeling, a coarse 36-km grid resolution was used for modeling the entire 

United States and a finer 12-km grid was used to model the eastern United States. 

The CMAQ model was run in the one-way nested grid mode. This allowed the larger 

outer domains to feed concentration data to the inner nested domain. One-way nesting is 

believed to be appropriate for the generally stagnant conditions experienced during North 

Carolina ozone episodes. Two-way nesting was not considered due to numerical and 

computational uncertainty associated with the technique. 

The horizontal coarse grid modeling domain boundaries were determined through a 

national effort to develop a common grid projection and boundary. A smaller 12-km grid 

modeling domain was selected in an attempt to balance location of areas of interest, such 

as ozone and fine particulate matter nonattainment areas, as well as Class 1 areas for 

regional haze. Processing time was also a factor in choosing a smaller 12-km grid, 

modeling domain. 

The coarse 36-km horizontal grid domain covers the continental United States. This 

domain was used as the outer grid domain for MM5 modeling with the CMAQ domain 

nested within the MM5 domain. Figure 5.4.1-1 shows the MM5 horizontal domain as the 

outer most, blue grid with the CMAQ 36-km domain nested in the MM5 domain. 

Figure 5.4.1-1. The MM5 horizontal domain is the outer most, blue grid, with the 

CMAQ 36-km domain nested in the MM5 domain. 
33 

To achieve finer spatial resolution in the VISTAS states, a one-way nested high 

resolution (12-km grid resolution) was used. Figure 5.4.1-2 shows the 12-km grid, 

modeling domain for the VISTAS region. The modeling results from this modeling 

domain are the basis for which the reasonable progress goals will be assessed. 

Figure 5.4.1-2. A more detailed view of the 12-km grid over the VISTAS region. 

5.4.2 Vertical Modeling Domain 

The CMAQ vertical structure is primarily defined by the vertical grid used in the MM5 

modeling. The MM5 model employed a terrain following coordinate system defined by 

pressure, using 34 layers that extend from the surface to the 100 mb. A layer-averaging 

scheme was used to generate 19 vertical layers for CMAQ to reduce the computational 

cost of the CMAQ simulations. The effects of layer averaging were evaluated in 

conjunction with the VISTAS modeling effort and found to have a relatively minor effect 

on the model performance metrics when both the 34 layer and a 19 layer CMAQ models 

were compared to ambient monitoring data. 

6.0 MODEL PERFORMANCE EVALUATION 

The initial modeling effort focused on evaluating previous regional air quality modeling 

applications and testing candidate model configurations for the SMOKE emissions and 

CMAQ model for the VISTAS 36-km and 12-km modeling domains. This effort resulted 

in a report recommending the model configuration for the annual emissions and air 

quality modeling, which is included as part of the VISTAS Emissions and Air Quality 

Modeling Protocol. The evaluation of the meteorological modeling configuration can be 

found in Appendix F.1, with a summary of the final meteorological and air quality 

modeling configuration in the modeling protocol contained in Appendix E and Appendix 

C, respectively. 

Air quality model performance for the 2002 modeling year was initially tested in 2004 

using an early version of the VISTAS emissions inventory. In keeping with the one-
34 

atmosphere objective of the CMAQ modeling platform, model performance was 

evaluated based on measured ozone, fine particles, and acid deposition in the Air Quality 

System (AQS), IMPROVE, Speciated Trends Network (STN), Southeastern Aerosol 

Research and Characterization (SEARCH), National Acid Deposition Program (NADP) 

and Clean Air Status and Trends Network (CASTNet) monitoring networks (Figure 6.0-

1). A detailed examination of the results was published in 2005 in the Journal of Air and 

Waste Management (see Appendix B.3) as well as being summarized in Appendix B.1. 

Figure 6.0-1: Monitoring Networks used for VISTAS 2002 model performance 

evaluation, and their location within the VISTAS 12km domain. 
35 

6.1 Modeling Performance Goals, and Criteria 

In 2004, VISTAS established model performance goals and criteria for components of 

fine particle mass (Table 6.1-1) based on previous model performance for ozone and fine 

particles. The USEPA’s 2007 modeling guidance for fine particulate matter at the time 

noted that PM models might not be able to achieve the same level of performance as 

ozone models. VISTAS’ evaluation considered several statistical performance measures 

and displays. Fractional bias and mean fractional error were selected as the most 

appropriate metrics to summarize model performance. Other metrics were also 

calculated and are included for IMPROVE monitors in the full model performance 

evaluation (Appendix F.2). 

Fractional Bias Mean Fractional 

Error 

Comment 

<15 percent <35 percent Goal for PM model performance based on ozone 

model performance, considered excellent performance 

<30 percent <50 percent Goal for PM model performance, considered good 

performance 

<60 percent <75 percent Criteria for PM model performance, considered 

average performance. Exceeding this level of 

performance indicates fundamental concerns with the 

modeling system and triggers diagnostic evaluation. 

Table 6.1-1: Established model performance goals and criteria for the component 

species of fine particle mass. 

Several graphic displays of model performance were prepared including: 

1. Scatter plots of predicted and observed concentrations and deposition by species, 

monitoring network, and month 

2. Time series plots of predicted and observed concentrations and deposition by 

species, monitoring site, and month 

3. Spatially average time series plots 

4. Time series plots of monthly fractional bias and error for a species, region, and 

network 

5. Performance goal plots (“soccer plots”) that summarize model performance by 

species, region, season 

6. Concentration performance plots (“bugle plots”) that display fractional bias or 

error as a function of concentration by species, region, monitoring network, and 

month 

The “soccer plots” and “bugle plots” are relatively new tools in model performance 

evaluations, and have recently been included as model performance evaluation displays 

in the USEPA’s 2007 modeling guidance for Ozone, PM2.5, and Regional Haze. Both 

“soccer plots” and “bugle plots” allow for a convenient way to examine model 

performance with respect to set goals and criteria. The bugle plots have the added benefit 

of adjusting the goals and criteria to consider the concentration of the species. Analysis 
36 

of “bugle plots” generally suggests that greater emphasis should be placed on 

performance of those components with the greatest contribution to PM mass and 

visibility impairment (e.g. sulfate and organic carbon) and that greater bias and error 

could be accepted for components with smaller contributions to total PM mass (e.g. 

elemental carbon, nitrate, and soil). 

6.2 VISTAS Domain-Wide Performance 

Further discussion of model performance in this document will focus on the comparison 

of observational data from the IMPROVE monitors and model output data from the 2002 

VISTAS actual annual air quality modeling. Focus is limited to the IMPROVE 

monitoring network as these sites are the locations used in projecting attainment visibility 

improvement goals in the Class I areas. 

The evaluation will primarily focus on the air quality model’s performance with respect 

to individual components of fine particulate matter (PM2.5), as good model performance 

of the component species will dictate good model performance of total or reconstituted 

fine particulate matter. Model performance of the total fine particulate matter and the 

resulting total light extinction will also be provided as a means to discuss the overall 

model performance for this Implementation Plan. 

In the analyses, mean fractional bias (error) is used in lieu of mean bias (error), to prevent 

low observations and model predictions from skewing the metrics. A full list of model 

performance statistics is found in Appendix F.2. The soccer and bugle plots for the all of 

the VISTAS IMPROVE monitors are included here for summary purposes. The goal and 

criteria levels used for regional haze model performance were 30% and 60%, 

respectively, for mean fractional bias and 50% and 75%, respectively, for mean fractional 

error. Plots have been developed for the average monthly concentrations and the 

performance statistics for all of the most significant light scattering component species 

(Sulfate, Nitrate, and Organic Carbon) for the 20% best days and 20% worst days. Plots 

for individual IMPROVE monitors associated with Tennessee Class I areas are included 

in Appendix F.2. 

The soccer plots of monthly concentrations (Figures. 6.2-1 and 6.2-2) show that values 

for nitrate generally fall outside of criteria performance thresholds (red box). Sulfates and 

organic carbon generally fall within goal thresholds (green box), with a couple of months 

falling just outside the goal thresholds but well within the criteria thresholds. In these 

figures, each point represents a month. Figure 6.2-3 contains separate soccer plots for 

each season. The seasonal plots emphasize poorer nitrate performance in the summer 

(does not even appear on the plots provided because performance is off scale with other 

constituents), when observed nitrate is quite low and predicted nitrate is even lower. 

When concentration is factored into performance criteria, nitrate performance improves 

with respect to mean fractional bias and mean fractional error (Figures 6.2-4 and 6.2-5) 

Additionally, performance assessed at the “one atmosphere” level was also deemed 

acceptable for ozone and particulate matter at various monitoring sites (STN, FRM, 
37 

CASTNet, etc.). Overall, VISTAS found the Base G2 modeling results to be 

representative and acceptable for use in modeling projection for ozone, particulate matter, 

and regional haze. 

All Sites CMAQ 12km - 2002 Monthly

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0

Fractional Bias

Mean Fractional Error 

Sulfate

Nitrate

Organics

Criteria

Goal

Figure 6.2-1: Soccer plot depicting both the mean fractional error and fractional bias for 

component concentration for all VISTAS sites. 

All Sites CMAQ 12km - 2002 Monthly

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

-75.0 -25.0 25.0 75.0

Fractional Bias

Mean Fractional Error 

Sulfate

Nitrate

Organics

Criteria

Goal

Figure 6.2-2: A zoomed view of the soccer plot depicting both the mean fractional error 

and fractional bias for component concentration for all VISTAS sites. 
38 

Figure 6.2-3: Seasonal soccer plots for all VISTAS IMPROVE monitors. 

All Sites CMAQ 12km - 2002 Monthly

0.0

50.0

100.0

150.0

200.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Average Concentration (μg/m3)

Mean Fractional Error 

Sulfate

Nitrate

Organics

Criteria

Goal

Figure 6.2-4: Bugle plot of the mean fraction bias for particulate matter and its component 

concentrations for all VISTAS sites. 

VISTAS IMPROVE Ga2a 12km - 2002 Spring (Apr May Jun) 

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

-75.0 -25.0 25.0 75.0

Fractional Bias

Fractional Error 

Sulfate

Nitrate

Ammon.

Organics

EC

Soils

PM25

PM10

CM

Criteria

Goal

VISTAS IMPROVE Ga2a 12km - 2002 Summer (Jul Aug Sep) 

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

-75.0 -25.0 25.0 75.0

Fractional Bias

Fractional Error 

Sulfate

Nitrate

Ammon.

Organics

EC

Soils

PM25

PM10

CM

Criteria

Goal

VISTAS IMPROVE Ga2a 12km - 2002 Fall (Oct Nov Dec) 

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

-75.0 -25.0 25.0 75.0

Fractional Bias

Fractional Error 

Sulfate

Nitrate

Ammon.

Organics

EC

Soils

PM25

PM10

CM

Criteria

Goal

VISTAS IMPROVE Ga2a 12km - 2002 Winter (Jan Feb Mar) 

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

-75.0 -25.0 25.0 75.0

Fractional Bias

Fractional Error 

Sulfate

Nitrate

Ammon.

Organics

EC

Soils

PM25

PM10

CM

Criteria

Goal
39 

All Sites CMAQ 12km - 2002 Monthly

-200.0

-150.0

-100.0

-50.0

0.0

50.0

100.0

150.0

200.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Average Concentration (μg/m3)

Mean Fractional Bias 

Sulfate

Nitrate

Organics

(+) Criteria

(+) Goal

(-) Goal

(-) Criteria

Figure 6.2-5: Bugle plot of mean fraction error for particulate matter and its component 

species for all VISTAS sites. 

6.3 Tennessee Class 1 Areas Performance 

The following section provides stack bar charts comparing observed fine particulate 

matter composition and modeled fine particulate matter composition. The charts have 

been split into two charts, with the first displaying the 20% best days followed by the 

chart for the 20% worst days. Stacked bar charts have been developed for the IMPROVE 

monitoring site located in the Great Smoky Mountains National Park (GSMNP1). (See 

Figure 1.4-1). The Great Smoky Mountains National Park IMPROVE monitor is used to 

represent the Joyce Kilmer-Slickrock Wilderness Area. 

The stacked bar chart allows a side-by-side comparison of the each day’s observed and 

modeled compositional and total light extinction. Within each bar the color codes are: 

• Yellow = light extinction due to sulfates (bextSO4) 

• Red = light extinction due to nitrates (bextNO3) 

• Green = light extinction due to organic carbon (bextOC) 

• Black = light extinction due to elemental (bextEC) 

• Brown = light extinction due to soil (bextSoil) 

• Grey = light extinction due to coarse mass (bextCM) 

The components are presented in the same order for both the observed (left hand bar) and 

modeled bar (right hand bar), so it easy to identify days when the prediction light 

extinction for the component differs from the observed. The total height of the bar 

provides the total reconstructed particulate matter light extinction value. Note the 
40 

differences in scale of the two charts; on the 20% best days modeled extinction is 

generally less than 20 Mm-1 different from measured values. For 20% worst days total 

differences between modeled and measured extinction varies from near zero to greater 

than 200 Mm-1. 

A cursory view of the stacked bars charts reiterates that sulfates are a large contributor to 

light extinction in the Great Smoky Mountains National Park on both 20% best days and 

20% worst days. The bar charts also suggest that organic carbon and nitrates are 

important on the 20% best days. The bar charts for the 20% best days reiterate the 

general under prediction seen in previous sections. The under prediction of sulfate on 

most of the 20% best days appears to be the crux of the under prediction, with the under 

prediction of nitrate observed on some days. On most 20% best days there is a small 

under prediction of SO4 and on some 20% best days there is a large nitrate under 

prediction. 

Comparing the 20% best day charts to the 20% worst days charts, one notices that the 

various components of particle pollution play a more prominent role in the 20% best days 

than with the 20% worst days. Also, the species make up on the 20% best days varies 

more widely compared to the 20% worst days. This suggests accurately modeling each 

species is especially important on the 20% best days. 

With the bar chart for the 20% worst days, it can be seen that model performance does 

improve across the sites. Light extinction due to sulfate prediction is better, but still falls 

short on some days. Light extinction due to organic carbon also becomes more important 

to total light extinction. Much like the sulfate component, the organic carbon component 

accuracy has improved performance over the 20% best day series, though some days are 

still under predicted. Overall, TDEC-APC found model performance to fall within 

acceptable limits for model performance. TDEC-APC further asserts the one atmosphere 

modeling performed by the VISTAS contractors is representative of conditions in the 

southeastern states and is applicable for use in setting reasonable progress goals for the 

Class I areas. 
41 

6.3.1 Great Smoky Mountain National Park 

Best 20% Obs (left) vs 2002gt2a (right) at GRSM1

0

10

20

30

40

50

60

70

80

90

100

8 11 14 23 32 38 50 59 62 68 89 116 194 257 269 299 302 308 320 326 341 347 356

Julian Date

bext (1/Mm)

bextCM

bextSOIL

bextEC

bextOC

bextNO3

bextSO4

Worst 20% Obs (left) vs 2002gt2a (right) at GRSM1

0

50

100

150

200

250

300

350

400

56 122 128 137 152 164 170 173 182 188 191 197 203 212 215 218 221 224 227 233 248 251 254 260

Julian Date

bext (1/Mm)

bextCM

bextSOIL

bextEC

bextOC

bextNO3

bextSO4

Figure 6.3-1: Stacked bar chart for GSMNP1 on the 20% best days (top) and 20% 

worst days (bottom). Observed composition is presented in the left hand bar, with 

modeled composition represented by the right hand bar. 
42 

7.0 LONG-TERM STRATEGY FOR TENNESSEE CLASS I AREAS 

As stated in section 1.3 above, the regional haze rule requires States to establish 

reasonable progress goals, expressed in deciviews, for visibility improvement at each 

affected Class I area in the State covering each (approximately) 10-year period until 

2064. This first set of reasonable progress goals must be met through measures contained 

in the state’s long-term strategy covering the period from the baseline until 2018. States 

are also to evaluate the effects of emissions from that state on Class I areas in other states. 

This section discusses development of Tennessee’s long-term strategy. 

7.1 Overview of the Long-Term Strategy Development Process 

a. The monitored data and modeling analyses cited in sections 2 and 5 above 

establish that for the VISTAS region, the key contributors to regional haze in 

the 2000-2004 baseline timeframe were large stationary sources of sulfur 

dioxide emissions. Keeping that key conclusion in mind, this section 

addresses the following questions: 

b. Assuming implementation of existing federal and state air regulatory 

requirements in Tennessee and the VISTAS region, how much visibility 

improvement, compared to the glidepath, would we expect to see at Class I 

areas in Tennessee between now and 2018? 

c. What additional emission controls represent BART in Tennessee? 

d. If additional emission reductions were needed, from what pollutants and 

source categories would the greatest visibility benefits be realized between the 

baseline and 2018? 

e. Narrowing down further, what are the geographic locations of the emissions 

which have the greatest impact on visibility in specific Class I areas? 

f. What types of emissions sources do we find in those geographic locations? 

g. Which specific individual sources in those geographic locations have the 

greatest visibility impacts at a given Class I area? 

h. What additional emission controls represent reasonable progress for those 

specific sources? 

i. Given the additional emission reductions expected from reasonable progress 

and BART, how much additional visibility improvement, compared to the 

glidepath is expected at Class I areas in Tennessee between the baseline and 

2018? 
43 

7.2 Expected Visibility Results in 2018 for TN Class I Areas under existing and 

planned emissions controls 

There are significant control programs being implemented between the baseline period 

and 2018. These programs are described in more detail below. 

7.2.1 Federal and State Control Requirements 

CAIR . CAIR will permanently cap emissions of SO2 and NOx from EGUs in the 

eastern United States by 2015. When fully implemented, CAIR will reduce SO2 

emissions from EGUs in these states by more than 70 percent, and NOx emissions by 

more than 60 percent, from 2003 levels. For TVA, the electric utility that operates coalfired EGUs in Tennessee, the following tables reflect the reductions expected: 

Emissions are aggregated at the facility level, rather than the unit level, as presented in 

the VISTAS emission inventories. 

Facility Name 2006 Heat Input (mmBtu) 2006 Net Output (GWH) 

Allen 55,883,612 5,291 

Bull Run 45,085,485 4,696 

Cumberland 185,663,408 18,743 

Gallatin 75,755,960 7,524 

John Sevier 49,978,425 5,044 

Johnsonville 88,835,413 7,555 

Kingston 107,167,323 10,378 

2006 heat inputs were obtained from Acid Rain Program monitoring data, and 2006 net outputs 

were obtained from TVA. 

Facility 

Name 

PM10 Emissions (tons) SO2 Emissions (tons) NOX Emissions (tons) 

2002 2009 2018 2002 2009 2018 2002 2009 2018 

Allen 866 981 981 19,030 21,263 21,263 14,510 2,061 2,061 

Bull Run 1,919 2,267 2,267 42,188 8,657 8,657 17,909 2,078 2,078 

Cumberland 1,831 1,690 1,690 16,661 24,009 24,009 49,939 5,762 5,762 

Gallatin 611 594 594 34,157 47,391 2,157 11,782 11,461 11,456 

John Sevier 160 151 163 35,488 32,327 1,846 10,234 10,109 1,574 

Johnsonville 5,561 2,661 2,661 108,789 22,216 22,216 23,879 13,329 1,333 

Kingston 3,574 3,766 4,056 77,571 71,120 4,037 26,089 3,238 3,633 

Emissions from 2002, 2009, and 2018 are from the VISTAS Base G/G2 emission inventory. 2002 

emissions are based on the VISTAS 2002 actual emissions inventory. 
44 

TVA Facility Existing SO2 Controls (2006) Projected SO2 Controls** 

Allen 

None. Plant burns low-sulfur fuel. 

2006 emission rate was 0.62 lb/mmBtu 

SO2. 

None planned prior to 2015. 

Bull Run None 

Wet Scrubber. Completion projected in 

2009. 

Cumberland Wet Scrubber No additional controls planned. 

Gallatin 

None. Plant burns low-sulfur fuel.2006 

emission rate was0.63 lb/mmBtu SO2.

None planned prior to 2015. 

John Sevier None 

Scrubber. Completion projected in 

2012. 

Johnsonville None 

 Testing is underway to determine the 

viability of reducing SO2 emissions from 

1.5 lb/mmBtu to 0.9 lb/mmBtu by 2015, 

based on combustion of lower sulfur coal. 

Kingston None 

Wet scrubber. Completion projected in 

2010. 

** Phase II CAIR requirements will require additional SO2 reductions within the TVA system by 

installing additional controls or by repowering or shutting down units.

NOx SIP Call. Phase I of the NOx SIP call applies to certain EGUs and large non-EGUs, 

including large industrial boilers and turbines, and cement kilns. Those states affected by 

the NOx SIP call in the VISTAS region have developed rules for the control of NOx 

emissions that have been approved by the USEPA. The NOx SIP Call has resulted in a 

66 percent reduction in summertime NOx emissions from large stationary combustion 

sources in Tennessee. For this analysis, we capped the emissions for NOx SIP callaffected sources at 2007 levels, and carried forward the capped levels for the 2009 and 

2018 future year inventories. 

North Carolina CSA. Under the act, enacted in 2002, coal-fired power plants in North 

Carolina must achieve a 77-percent cut in NOx emissions by 2009 and a 73-percent cut in 

SO2 emissions by 2013. This legislation establishes annual caps on both SO2 and NOx 

emissions for the two primary utility companies in North Carolina, Duke Energy and 

Progress Energy. These reductions must be made in North Carolina, and allowances 

cannot be sold. 

Georgia Multipollutant Bill. Georgia enacted regulations in summer 2007 to require coal 

fired power plants in Georgia to reduce SO2 by approximately 90%, NOx by 

approximately 50%, and mercury by an estimated 75-85% by 2015. Reductions will 

affect 21 units at seven facilities. The rule requires specific controls on specific units 

according to a specific schedule. This is not a cap and trade rule, thus there are no 

allowances to be banked, sold or traded. 

Consent Agreements. Under a settlement agreement, by 2008, Tampa Electric will install 

permanent emissions-control equipment to meet stringent pollution limits; implement a 

series of interim pollution-reduction measures to reduce emissions while the permanent 
45 

controls are designed and installed; and retire pollution emission allowances that Tampa 

Electric or others could use, or sell to others, to emit additional NOx, SO2, and PM. 

Virginia Electric and Power Co. agreed to spend $1.2 billion by 2013 to eliminate 

237,000 tons of SO2 and NOx emissions each year from eight coal-fired electric 

generating plants in Virginia and West Virginia. 

AEP [US District Court for the Southern District of Ohio, Eastern Division]: In October 

2007 American Electric Power agreed to spend $4.6 billion dollars to eliminate 72,000 

tons of NOx emissions each year by 2016 and 174,000 tons of SO2 emissions each year 

by 2018 from sixteen coal-fired power plants located in Indiana, Kentucky, Ohio, 

Virginia and West Virginia. 

A 2002 agreement calls for Gulf Power to upgrade its operation to cut NOx emission 

rates by 61 percent at its Crist generating plant by 2007, with major reductions beginning 

in early 2005. The Crist plant is a significant source of NOx emissions in the Pensacola 

area. 

One-hour ozone SIPs (Atlanta / Birmingham / Northern Kentucky). New SIPs have 

been submitted to the USEPA to demonstrate attainment of the one-hour ozone National 

Ambient Air Quality Standard (NAAQS). These SIPs require NOx reductions from 

specific coal fired power plants and address transportation plans in these cities. 

NOx RACT in 8-hour Nonattainment Area SIPs. North Carolina’s SIP for the 

Charlotte / Rock Hill / Gastonia nonattainment area includes Reasonable Achievable 

Control Technology (RACT) for NOx for two facilities located in the nonattainment 

area: Philip Morris USA and Norandal USA. These controls were also modeled in 

2018. Additional RACT controls may be realized as other companies subject to 

RACT complete the determination, but RACT-level controls were assumed for just 

these two sources. 

Heavy Duty Diesel (2007) Engine Standard (for on-road trucks and buses). USEPA 

set a PM emissions standard for new heavy-duty engines of 0.01 grams per brakehorsepower-hour (g/bhp-hr), to take full effect for diesel engines in the 2007 model 

year. This rule also includes standards for NOx and non-methane hydrocarbons 

(NMHC) of 0.20 g/bhp-hr and 0.14 g/ bhp-hr, respectively. These NOx and NMHC 

standards will be phased in together between 2007 and 2010, for diesel engines. 

Sulfur in diesel fuel must be lowered to enable modern pollution-control technology 

to be effective on these trucks and buses. USEPA will require a 97 percent reduction 

in the sulfur content of highway diesel fuel from its current level of 500 parts per 

million (low sulfur diesel, or LSD) to 15 parts per million (ultra-low sulfur diesel, or 

ULSD). 

Tier 2 Tailpipe (On-road vehicles). USEPA mobile rules include the Tier 2 fleet 

averaging program, modeled after the California LEV II standards. Manufacturers can 
46 

produce vehicles with emissions ranging from relatively dirty to zero, but the mix of 

vehicles a manufacturer sells each year must have average NOx emissions below a 

specified value. Tier 2 standards became effective in the 2005 model year. 

Large Spark Ignition and Recreational Vehicle Rule. USEPA has adopted new 

standards for emissions of oxides of nitrogen (NOx), hydrocarbons (HC), and carbon 

monoxide (CO) from several groups of previously unregulated nonroad engines. 

Included in these are large industrial spark-ignition engines and recreational vehicles. 

Nonroad spark-ignition engines are those powered by gasoline, liquid propane gas, or 

compressed natural gas rated over 19 kilowatts (kW) (25 horsepower). These engines 

are used in commercial and industrial applications, including forklifts, electric 

generators, airport baggage transport vehicles, and a variety of farm and construction 

applications. Nonroad recreational vehicles include snowmobiles, off-highway 

motorcycles, and all-terrain-vehicles. These rules were initially effective in 2004 and 

will be fully phased-in by 2012. 

Nonroad Diesel Rule. This rule sets standards that will reduce emissions by more 

than 90 percent from nonroad diesel equipment, and reduce sulfur levels by 99 

percent from current levels in nonroad diesel fuel starting in 2007. This step will 

apply to most nonroad diesel fuel in 2010 and to fuel used in locomotives and marine 

vessels in 2012. 

Industrial Boiler/Process Heater/RICE MACTs. The USEPA issued final rules to 

substantially reduce emissions of toxic air pollutants from industrial, commercial and 

institutional boilers, process heaters and from stationary reciprocating internal 

combustion engines (RICE). These rules reduce emissions of a number of toxic air 

pollutants, including hydrogen chloride, manganese, lead, arsenic and mercury by 

2009. This rule also reduces emissions of SO2 and PM in conjunction with the toxic 

air pollutant reductions. The applied Maximum Achievable Control Technology 

(MACT) control efficiencies were 4 percent for SO2 and 40 percent for PM10 and 

PM2.5. The USEPA’s industrial boiler MACT rules were vacated on June 8, 2007. 

The VISTAS states decided to leave these controls in the modeling since it is believed 

that by 2018, the USEPA will have re-promulgated a boiler MACT rule or states will 

have addressed the issue through state rule making. 

Combustion Turbine MACT. The projection inventories do not include the NOx cobenefit effects of the MACT regulations for Gas Turbines or stationary Reciprocating 

Internal Combustion Engines, which USEPA estimates to be small compared to the 

overall inventory. 

VOC 2-, 4-, 7-, and 10-year MACT Standards. Various point source MACTs and 

associated emission reductions were implemented. Reductions occurring before 2002 

were assumed to be accounted for in the 2002 base year inventory. 
47 

7.2.2 Additional State programs to reduce emissions 

In addition to accounting for specific emission reductions due to ongoing air pollution 

programs as required under the regional haze rule’s section 308 (d)(3)(v)(A), states are 

also required to consider the air quality benefits of measures to mitigate the impacts of 

construction activities [section 308(d)(3)(v)(B)] and agricultural and forestry smoke 

management [section 308(d)(3)(v) (E)]. These state measures are discussed in more 

detail in Section 7.9 and Appendix H. 

7.2.3 Projected 2009 and 2018 Emissions Inventories 

The inventories for 2009 and 2018 account for post-2002 emission reductions from 

promulgated and proposed federal, state, local, and site-specific control programs as of 

July 1, 2004. In general, emissions inventories were developed for 2009 and 2018 using 

current control information in Tennessee. 

For EGUs, IPM results were adjusted based on state and local agencies’ knowledge of 

planned emission controls at specific EGUs. These updates are documented in the 

MACTEC emissions inventory report “Documentation of the Base G2 and Best & Final 

2002 Base Year and 2009 and 2018 Projection Year Emission Inventories for VISTAS” 

dated March 2008. (See Appendix D-5b) 

For non-EGUs, VISTAS used recently updated growth and control data consistent with 

the data used in USEPA’s CAIR analyses supplemented by state and local agencies’ data 

and updated forecasts from the Department of Energy (DOE). 

Area source controls were estimated using known state level Stage I controls on gasoline 

dispensing facilities and open burning estimates, as well as controls used to project 

emissions for the USEPA’s Heavy Duty Diesel rulemaking and for the CAIR rulemaking. 

Mobile source controls included local controls underlying the 2002 baseline inventory 

(vehicle emission inspection, Stage II vapor recovery, anti-tampering, etc.) with changes 

based on specific State input. The future year inventories were developed by running the 

MOBILE 6.2 model for each year modeled. The future year emissions for the off-road 

mobile sources included in the USEPA NONROAD model were estimated by running the 

model for each future year. For the other off-road mobile source categories, control data 

and projections for 1996, 2010, 2015, and 2020 were obtained form the USEPA’s CAIR 

Technical Support Document and straight-line projections were used to estimate 2009 

and 2018 levels. 

The following bar charts show expected decreases in emissions of SO2 and NOx across 

the VISTAS states from 2002 through 2018. (Similar charts for other visibility impairing 

pollutants are contained in Appendix H). Note that for SO2 emissions in particular, 

which are the largest contributors to haze, emissions from electric generating facilities are 

expected to decrease dramatically (70 percent) between 2002 and 2018. However, even 

after implementation of CAIR, EGU emissions are projected to remain the largest 
48 

contributor to haze, comprising more than half of remaining SO2 emissions in every 

state. 

Annual SO2 Emissions by Source Sector

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

AL

FL

GA

KY

MS

NC

SC

TNVA

WV

Tons per year

FIRES

AREA

NONROAD

ONROAD

NONEGU

EGU

2002 Actual EGU and Fires

2002 Typical EGU and Fires

2009

2018

Figure 7.2.3-1. Annual SO2 emissions for 2002, 2009, and 2018 in the VISTAS 

States. 

Annual NOx Emissions by Source Sector

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

AL

FL

GA

KY

MS

NC

SC

TN

VA

WV

Tons per year

FIRES

AREA

NONROAD

ONROAD

NONEGU

EGU

2002 Actual EGU and Fires

2002 Typical EGU and Fires

2009

2018

v

Figure 7.2.3-2. Annual NOx emissions in 2002, 2009, and 2018 in the VISTAS States. 
49 

Summary of Emissions Inventories for 2009 and 2018

Tables 7.2.3-1 and 7.2.3-2 are summaries of the 2009 and 2018 Base G4 (Best and Final) 

emission inventory, respectively. The complete inventory and discussion of the 

methodology is contained in Appendix D. 

Table 7.2.3-1. 2009 Base G4 (B&F) Emissions Inventory Summary for TN. 

Source Category VOC NOx PM2.5 PM10 NH3 SO2 

Point 74,430 123,919 40,459 50,189 2,067 320,374 

Area 155,194 18,872 45,573 227,787 35,331 30,679 

On-Road Mobile 115,181 151,912 2,751 4,206 7,782 1,076 

Non-Road Mobile 55,358 86,641 5,557 5,877 48 5,651 

Biogenics 894,214 18,081 0 0 0 0 

TOTAL 1,294,377 399,425 94,340 288,059 45,228 357,780 

Table 7.2.3-2. 2018 Base G4 (B&F) Emissions Inventory Summary for TN. 

Source Category VOC NOx PM2.5 PM10 NH3 SO2 

Point 93,432 94,234 46,680 57,940 2,454 169,354 

Area 183,110 20,002 48,265 248,086 36,376 32,073 

On-Road Mobile 67,324 69,385 1,544 3,092 9,021 948 

Non-Road Mobile 45,084 70,226 4,403 4,672 55 5,207 

Biogenics 894,214 18,081 0 0 0 0 

TOTAL 1,283,164 271,928 100,892 313,790 47,906 207,582 

7.2.4 Model Results for the 2018 Inventory Compared to the Uniform Rate of 

Progress Glidepaths for Tennessee Class I Areas 

Using 2000 - 2004 IMPROVE monitoring data, the deciview values for the 20 percent 

best days in each year are averaged together, producing a single average deciview value 

for the best days. Similarly, the deciview values for the 20 percent worst days in each 

year are averaged together, producing a single average deciview value for the worst days. 

The average values represent the current visibility conditions. 

The predicted visibility improvement is calculated by comparing the 2002 typical year 

modeling results for the 12-km grid to the 2018 12–km modeling results to develop a 

relative reduction factor. This factor is then applied to the current visibility condition 

values to estimate the future visibility. Detailed discussions about how the relative 

reduction factors are calculated can be found in Appendix G. 

Figures 7.2.4-1 through 7.2.4- illustrate the predicted visibility improvement by 2018 

resulting from the Best & Final inventory that represents implementation of existing 

federal and state regulations, compared to the reasonable progress assessment for Great 

Smoky Mountains National Park and Joyce Kilmer-Slickrock Wilderness Area. In the 

figures, the red lines show the uniform rate of progress to natural conditions in 2064 for 

the 20 percent worst days or 20 percent best days, and the incremental deciview changes 

resulting form a uniform rate of progress between current and natural conditions in 2064. 
50 

The purple lines show the improvement expected from existing and planned emission 

controls during the period of the first long term strategy. As previously stated, the 

visibility improvement for Joyce Kilmer – Slickrock Wilderness Area is represented by 

the results for the Great Smoky Mountains National Park. Natural background visibility 

calculations are based on EPA’s default natural conditions estimates (Method 1) as 

explained in Appendix B-4 (page 31). 

(Method 1Prediction = Default Calculation per EPA Guidance) 

Figure 7.2.4-1. Reasonable progress assessment compared to Uniform Rate of Progress 

for 20% worst days at Great Smoky Mountains National Park.

(Method 1 Prediction = Default Calculation per EPA Guidance) 

Figure 7.2.4-2. Reasonable progress assessment compared to Uniform Rate of Progress 

for 20% worst days at Joyce Kilmer-Slickrock Wilderness Area. 

Reasonable Progress Assessment

Great Smoky Mountains - 20% Worst Data Days

NEW IMROVE EQUATION, 12 km grid

30.28 28.67

25.79

22.59

19.38

16.18

12.97

11.05

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Uniform rate of progress = 4.5 dv by 2018

Reasonable Progress Assessment

Joyce Kilmer-Slickrock - 20% Worst Data Days

NEW IMROVE EQUATION, 12 km grid

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Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform rate of progress = 4.5 dv by 2018
51 

(Method 1 Prediction = Default Calculation per EPA Guidance) 

Figure 7.2.4-3 Reasonable progress assessment for 20 % best days at 

Great Smoky Mountains National Park 

Visibility Improvement at Joyce Kilmer Wilderness is represented by Great Smoky Mountains. 

Figure 7.2.4-4 CMAQ 2018 results at Joyce Kilmer-Slickrock Wilderness Area. 

Note that at both Class I areas in Tennessee, visibility improvements on the 20 percent 

worst days are expected to be better than the uniform rate of progress glidepath by 2018 

based solely on reductions from existing and planned emissions controls. For example, at 

Great Smoky Mountains, a 4.5 dv improvement in visibility would meet uniform rate of 

progress in 2018; expected emissions reductions by 2018 are projected to achieve a 6.78 

dv improvement. In fact, as illustrated in Figure 7.2.4-5, visibility improvements at all 

the VISTAS mountain Class I areas and most of the coastal Class I areas are projected to 

be better than the uniform rate of progress. In Figure 7.2.4-5, the percentage of the target 

reduction achieved for the Great Smoky Mountains National Park using the new 

IMPROVE equation is 150 percent. This means that the rate of improvement is 50 

percent greater than the uniform rate of progress by 2018. 

In addition to improving visibility on the 20 percent worst visibility days, states are also 

required to protect visibility on the 20 percent best days at the Class I areas. As 

illustrated in Figure 7.2.4-6, visibility on the 20 percent best days is projected to improve 

in 2018 at all VISTAS Class I areas as a result of the 2018 emissions reductions. In 

Figure 7.2.4-6 the percentage of the target achieved for the Great Smoky Mountains 

National Park is about -10 percent. Zero percent change would mean no change in 

visibility; -10 percent means that visibility is better than no change, or a 10 percent 

improvement (values lower than current conditions). 

Reasonable Progress Assessment 

Great Smoky Mountains - 20% Best Days

New Improve Equation, 12 km grid

13.58 13.58 13.58

13 .11 12 .11 4.54

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Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction
52 

Figure 7.2.4-5. Projected visibility improvement on 20 percent worst visibility days 

at VISTAS and neighboring Class I areas for 2018 (12 km grid) 

Figure 7.2.4-6. Projected visibility improvement on 20 percent best visibility days at 

VISTAS and neighboring Class I areas for the 2018 Base G2a CMAQ run 

2018 Base G2a CMAQ Visibility Projection for 20% Best Days

-50%

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VISTAS non-VISTAS

CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm

2018 Base G2a CMAQ Visibility Projection for 20% Best Days

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CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm
53 

The expected change in visibility at Great Smoky Mountains between 2000-2004 

baseline conditions on the 20 percent worst days, and 2018 projections is illustrated in 

Figure 7.2.4-7 (upper picture). In contrast, natural background visibility for the 20 

percent worst days is illustrated in the bottom picture in Figure 7.2.4-7. These images 

were generated using WINHAZE, a photographic imaging tool that accounts for the 

effect of concentrations of the fine particle components and relative humidity on 

visibility. These images illustrate that notable improvements in visibility are expected by 

2018 and that significantly greater improvements are needed to reach natural background 

conditions. 

Figure 7.2.4-7. Visibility improvement on 20% haziest days at Great Smoky 

Mountains between 2000-2004 baseline conditions (upper left) and 2018 projected 

visibility (upper right). (Bottom) Projected visibility on 20% haziest days for 

natural background 

20% Haziest Days at Great Smoky Mtns

Natural Background Visibility Great Smoky Mtns
54 

7.3 Relative Contribution from International Emissions to Visibility Impairment in 

2018 at VISTAS Class I areas 

Emissions from Mexico, Canada, Central America, Asia, and Africa contribute to PM2.5 

loadings and visibility impairment at Class I areas in the continental United States. To 

evaluate the relative contribution of international emissions to visibility at Class I areas in 

the southeastern United States, VISTAS used a combination of modeling results from the 

global three-dimensional chemical transport model (GEOS-Chem) and CMAQ. VISTAS 

used the GEOS-Chem global model to generate initial and boundary conditions for the 

CMAQ modeling domain. GEOS-Chem was run for the 2002 modeling year using a 4 x 

5 degree horizontal grid resolution and a 3-hour temporal resolution. Because emissions 

were based on monthly averages, the model does not capture the episodic variability in 

emissions. The GEOS-Chem outputs were used to calculate initial and boundary 

conditions for the national CMAQ modeling domain. The national CMAQ domain 

included portions of Canada and Mexico, so emissions for these countries were included 

within the national CMAQ modeling domain or as part of the boundary conditions 

outside the national modeling domain, as appropriate. 

Two complementary methods were used to calculate the impact of international 

emissions at Class I areas. Because the international emissions inventory used in 

GEOS-Chem did not distinguish between wildfires and anthropogenic fires, all 

international fire emissions were assumed to be wildfire, and the contributions 

attributable to international anthropogenic fires are underestimated in these projections. 

1) International emissions are represented by the difference between two GEOSChem runs. In the first run United States anthropogenic emissions were 

removed, and in the second run both United States and international 

anthropogenic emissions were removed. The difference represents international 

anthropogenic emissions, in the absence of United States anthropogenic emissions 

(e.g. compared to 2064 levels). Harvard University provided GEOS-Chem results 

to VISTAS for 2002 international contribution on 4 x 5 degree grid. Concurrently 

Harvard modeling for the Electric Power Research Institute (EPRI) provided 

GEOS-Chem results for 2001 international contribution on a 1 x 1 degree grid 

scale. 

2) International emissions are represented by the difference between two CMAQ 36-

km simulations, both using an earlier version of the 2018 emissions and boundary 

conditions from GEOS-Chem. In the first CMAQ run, all global natural and 

anthropogenic emissions in 2018 are active. In the second CMAQ run, only 

global (United States and international) natural emissions are active. Here the 

impacts of international emissions are compared against 2018 conditions rather 

than natural background conditions. 
55 

VISTAS compared its results to results from other RPOs on the impact of 

international emissions and boundary conditions on visibility at Class I areas in 2002. 

The results were similar. 

As illustrated in Figure 7.3-1 for annual average contributions to sulfate at VISTAS 

and neighboring Class I areas, the estimated international contributions are higher at 

Class I areas near the Canadian and Mexican borders and along the eastern coast. 

The estimated international contribution is higher using CMAQ than in the GEOSChem runs because the grid scale is finer (more accurate dispersion of emissions) and 

because the background atmosphere includes loadings from current United States 

anthropogenic emissions (greater photochemical activity). Similar charts for nitrate 

and organic carbon mass, for impacts on 20 percent worst visibility days, and for 

impacts of international emissions on calculated light extinction are included in 

Appendix G. 

Figure 7.3-1. Estimated international emissions contributions to sulfate at VISTAS 

and neighboring Class I areas. 

In Figure 7.3-2 CMAQ projections of contributions from international emissions to PM 

mass on 20 percent worst visibility days in 2002 at Great Smoky Mountains National 

Park are compared to United States domestic contributions to PM components at the site 

on those days. 

Transboundary Man-Made Impacts

0.0

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(NH4) 2SO 4 Conc. (

μg/m 3

)

GEOS_4x5 GEOS_1x1 CMAQ
56 

Figure 7.3-2. PM component concentrations from US domestic sources on 20 

percent worst visibility days in 2002 (left bars) and CMAQ-simulated international 

contributions (right bars) at Great Smoky Mountains National Park. 

Although VISTAS assessed impacts from international emissions as the Class I areas, 

these modeling runs were to provide information to the states to understand what the 

potential impacts may be at their Class I areas. Since good projected emissions for areas 

outside of the continental United States are not readily available, there is no real way to 

assess what the impacts from international emissions would be for 2018, other than to 

assume that it would be approximately same amount as for the baseline current 

conditions. Therefore, TDEC-APC will not be accounting for international emissions in 

setting the 2018 reasonable progress goals for its Class I areas. 

Nevertheless, as the atmosphere becomes closer to natural background conditions in the 

future, the incremental contribution from international emissions will become more 

important. The information is included in this SIP documentation to provide reference 

for future assessments of reasonable progress. 

GRSM1

Domestic and International Aerosol Mass by Species

20% Worst Days (2002)

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Mass (μg/m 3

)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt
57 

7.4 Relative Contributions to Visibility Impairment: Pollutants, Source Categories, 

and Geographic Areas 

An important step toward identifying further reasonable progress measures is to identify 

the key pollutants contributing to visibility impairment at each Class I area. To 

understand the relative benefit of further reducing emissions from different pollutants, 

source sectors, and geographic areas, VISTAS engaged the Georgia Institute of 

Technology to perform emission sensitivity model runs using CMAQ. Emissions 

sensitivities were initially performed for three episodes representing winter and summer 

conditions: Jan 2002, July 2001, and July 2002. These runs used the initial 2018 

projections inventory and considered 30 percent reductions from specific pollutants, 

source categories, and geographic areas. Emissions sensitivities were repeated using a 

preliminary 2009 projection inventory and two month-long episodes from 2002: Jun 1-

Jul 10 and Nov 19 – Dec 19. Emissions in 2009 were reduced by 30 percent for each 

pollutant sensitivity run. The pollutant contributions that were evaluated were: 

• SO2 from EGU sources in each VISTAS state, other RPOs in the VISTAS 12 km 

grid, and Boundary Conditions from outside the 12 km domain. 

• SO2 from non-EGU point sources in each VISTAS state, other RPOs, and 

Boundary Conditions 

• NOx from ground level sources (on-road plus off-road plus area) in each 

VISTAS state and other RPOs. In the VISTAS states, these reductions were only 

applied to specific counties that were of concern for 8-hour ozone nonattainment. 

• NOx from point (EGU plus non-EGU) sources in each VISTAS state and other 

RPOs 

• NH3 from all sources in VISTAS and other RPOs 

• Volatile Organic Compounds from anthropogenic and biogenic sources in the 12 

km modeling domain 

• Primary Carbon from all ground level sources in each VISTAS state and other 

RPOs. In the VISTAS states, these reductions were only applied to specific 

counties that were of concern for PM2.5 nonattainment. 

• Primary Carbon from all point sources in each VISTAS state and other RPOs 

• Primary Carbon from all fires in each VISTAS state and other RPOs 

Figure 7.4-1 below shows the average of the 20 percent worst visibility days for Great 

Smoky Mountains National Park. Responses for 20 percent worst days were calculated 

by averaging the responses of the 20 percent worst days that were modeled in the two 

episodes. These results were used to understand the relative importance of pollutant 

contributions at this site. 
58 

Figure 7.4-1. CMAQ projections of visibility responses on 20 percent worst days at 

Great Smoky Mountains to 30 percent reductions from a 2009 inventory for 

visibility-reducing pollutants in different source categories and geographic areas. 

As Figure 7.4-1 illustrates the greatest visibility benefits on the 20 percent worst days for 

the Great Smoky Mountains National Park are projected to result from further reducing 

SO2 from EGUs. Benefits are projected from SO2 reductions from EGUs in several 

VISTAS states including Alabama, Georgia, Kentucky, North Carolina, South Carolina, 

Tennessee, Virginia, and West Virginia. Contributions from other RPOs and from the 

boundary conditions are comparatively small and the greatest benefits would likely be 

from further EGU reductions within the VISTAS states. Additional, smaller benefits are 

projected from additional SO2 emission reductions from non-utility, industrial point 

sources. The pattern of relative SO2 contributions from non-EGUs among the various 

VISTAS states is similar to the pattern of relative SO2 contributions from EGUs. 

Because ammonium nitrate is a small contributor to PM2.5 mass and visibility 

impairment on the 20 percent worst days at the mountain Class I areas, the benefits of 

reducing NOx and NH3 emissions at these sites are small. 

VOCs do contribute to visibility impairment, but as shown in the chart above, this 

contribution is from biogenic sources such as vegetative emissions. Controlling 

anthropogenic sources of VOC emissions has little if any visibility benefit at the Great 

Smoky Mountains National Park. Reducing primary carbon from point sources, ground 

level sources or fires are projected to have small to no visibility benefit. This is 

consistent with the monitoring data that shows that most of measured organic carbon is 

secondary in origin and primary carbon is only a small fraction of the total measured 

Great Smoky Mtns, TN (20% Worst Days)

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WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Great Smoky Mtns, TN (20% Worst Days)

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59 

carbon (Appendix B). Reducing carbon from fires was not found to be effective because 

there was little fire activity at these sites on the days modeled in the sensitivity analyses. 

Note that these results from the emission sensitivity runs are consistent with the 

conclusions drawn from the 2000-2004 baseline monitoring data (see section 2.4). The 

results indicate that sulfate is the dominant contributor to visibility impairment on the 20 

percent worst days at all sites, and that ammonium nitrate may be important for sites 

where the 20 percent worst days occur in the winter. TDEC-APC concludes that 

reducing SO2 emissions from EGU and non-EGU point sources in the VISTAS states 

would have the greatest visibility benefits for the Tennessee Class I areas. Contributions 

from the Midwest RPO and MANE-VU are greater at the Class I areas bordering these 

RPOs. Contributions from outside the VISTAS 12-km modeling domain are more 

important for the coastal Class I areas. These results are consistent with the CMAQ 

model results indicating that contributions from international emissions to visibility 

impairment at VISTAS Class I areas are greater closer to the boundaries of the modeling 

domain (see summary in section 7.3 and further discussion in Appendix G). 

Emissions sensitivities were also run for SO2 emissions from EGU point sources in 

VISTAS states, non-EGU industrial point sources in VISTAS states, point sources in 

non-VISTAS states, and boundary conditions for the VISTAS modeling domain. The 

2018 Base G inventory was run for the entire annual modeling year and SO2 emissions 

were zero-ed out in separate CMAQ runs for each SO2 source category listed in the 

previous sentence. These 2018 annual emission sensitivity runs confirm the conclusions 

from the 2009 episode runs. As illustrated in Figure 7.4.2, on the 20% worst days, even 

after implementation of the Clean Air Interstate Rule, in 2018 further reduction in 

emissions from the utility sector would have greater benefit to visibility at Great Smoky 

Mountains than reductions from the industrial sectors. These conclusions apply the total 

source category and to determine relative benefits of reductions from specific utility or 

industrial sources, additional source-specific modeling would be required. 

Figure 7.4-2. 2018 Annual Base G Emissions: Responses to SO2 Zero-out 

Sensitivity Runs. 

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60 

7.5 What Control Determinations Represent Best Available Retrofit Technology 

(BART) for Individual Sources? 

Section 169A of the CAA directs States to assess certain large, older emission sources for 

additional controls in order to address visibility impacts. States are directed to conduct 

BART determinations for such sources in specific source categories, and which 

contribute to visibility impairment in Class I areas. The 1999 regional haze rule includes 

the BART requirement, and directs States to include BART in their regional haze SIPs. 

On July 6, 2005, USEPA published a revised final rule, including Appendix Y to 40 CFR 

Part 51, the Guidelines for BART Determinations Under the Regional Haze Rule

(hereinafter referred to as the “BART Guidelines”) that provides direction to states on 

determining which of these sources should be subject to BART, and how to determine 

BART for each source. 

A BART-eligible source is one which has the potential to emit 250 tons or more of a 

visibility-impairing air pollutant, was put in place between August 7, 1962 and August 7, 

1977, and whose operations fall within one or more of 26 specifically listed source 

categories. Under the CAA, BART is required for any BART-eligible source that a State 

determines ‘‘emits any air pollutant which may reasonably be anticipated to cause or 

contribute to any impairment of visibility in any such area.’’ 

For those sources subject to BART, Section 169A(g)(7) of the CAA requires that States 

must consider the following factors in making BART determinations: (1) the costs of 

compliance, (2) the energy and non-air quality environmental impacts of compliance, (3) 

any existing pollution control technology in use at the source, (4) the remaining useful 

life of the source, and (5) the degree of improvement in visibility which may reasonably 

be anticipated to result from the use of such technology. 

7.5.1 BART-Eligible Sources in Tennessee 

The following is a list of facilities with BART-eligible sources in Tennessee. TDEC-APC 

developed this list by examining the 2002 NEI data summary. The list was 

electronically sorted to retrieve sources that had emissions of visibility impairing 

pollutants that were greater than or equal to 250 tons per year. The pollutants included 

sulfur dioxide, nitrogen oxides, particulate matter, and volatile organic compounds as set 

forth in the proposed BART rule in the FR Vol. 69, No. 87, May 5, 2004 on page 25216. 

Per the final BART rule in the FR Vol. 70, No.128 on page 39112 and page 39160, the 

list was expanded to include ammonia. 

Next, the sources of the aforementioned emission magnitude were examined by SIC code 

and NAICS code to determine if they fell under one of the BART 26 source categories. 

Most large VOC sources were excluded from BART because they were not in the BART 

specific source categories. Using this electronically reduced list, a file review and 

dialogue with the identified source was used to determine if the source construction date 

was in the 1962 to 1977 BART period. That final list was then evaluated under BART 

exemption modeling that is already discussed in the Regional Haze SIP that was taken 
61 

out for public comment. See Appendix L for a detailed description of each BARTeligible emission unit: 

FACILITY NAME 

Aluminum Company of America (Alcoa) – South Plant 

DuPont White Pigment and Mineral Products (Humphreys County) 

Eastman Chemical Company – Tennessee Operations 

E I DuPont de Nemours and Company, Inc. (Old Hickory) 

E I DuPont de Nemours and Company, Inc. (Shelby County) 

Holston Army Ammunition Plant 

Intertrade Holdings, Inc. 

Liberty Fibers Corporation

Lucite International 

Owens Corning 

Packaging Corporation of America (PCA) 

PCS Nitrogen 

Tennessee Valley Authority (TVA) – Bull Run Fossil Plant 

Tennessee Valley Authority (TVA) – Cumberland Fossil Plant 

Zinifex 

Weyerhaeuser Corporation (now Domtar Paper Company) - Sullivan County 

The BART-eligible sources were identified using the methodology in the BART 

Guidelines. 

• One or more emissions units at the facility fit within one of the 26 categories 

listed in the BART Guidelines; 

• The emission unit(s) were in existence on August 7, 1977 and began operation at 

some point on or after August 7, 1962; and 

• The limited potential emissions from all emission units identified in the previous 

two bullets emission units were greater than 250 tons or more per year of any of 

these visibility-impairing pollutants: SO2, NOx, and PM10. 

The BART Guidelines recommend addressing these visibility-impairing pollutants: SO2, 

NOx, and particulate matter, and suggest that States use their best judgment in 

determining whether to address VOC or ammonia emissions. TDEC-APC addressed 

SO2 and NOx, and used particulate matter less than 10 microns in diameter (PM10) as an 

indicator for particulate matter to identify BART-eligible units, as the BART Guidelines 

recommend. As discussed in detail in Appendix L (BART related documentation 

narrative), VISTAS modeling demonstrated that VOCs and ammonia from point sources 

are not visibility-impairing pollutants. For this reason TDEC-APC did not evaluate 

emissions of VOCs. However, ammonia emissions were evaluated only from one major 

source of ammonia (i.e., PCS Nitrogen) in the BART determinations. 
62 

7.5.2 Determination of Sources Subject to BART in Tennessee 

Under the BART Guidelines, TDEC-APC may consider exempting some sources from 

BART if we find that they do not cause or contribute to visibility impairment in a Class I 

area. In accordance with the BART guidelines, TDEC-APC chose to perform sourcespecific analyses to determine which sources cause or contribute to visibility impairment 

using the CALPUFF model. The CALPUFF modeling protocol used for determining 

which facilities are subject to BART is included in Appendix L. 

TDEC-APC also examined the appropriateness of the 0.5 deciview threshold for potential 

“clusters” of BART sources that might be impacting a Class I area in a cumulative 

manner, and did not find evidence that an exemption threshold less than the 0.5 deciview 

threshold should be used. Appendix H’s main document describing reasonable progress 

has additional information in Section 5.0 regarding individual source impacts upon Class 

I areas. In accordance with the Guidelines, a contribution threshold of 0.5 deciviews was 

used for determining which sources were subject to BART. 

Tennessee initially identified sixteen (16) facilities with BART-eligible sources. Since 

the time that the BART-eligible sources were identified, Liberty Fibers Corporation has 

permanently shut down, and the BART-eligible boilers located at the facility have been 

dismantled. Additionally, Intertrade Holdings, Inc. has permanently shut down the acid 

plant that was determined to be BART-eligible (a reduction of sulfur dioxide of 

approximately 374 tons per year). Holston Army Ammunition Plant has requested a 

permit limit of 249 tons per year for the emissions units that make up their acid plant 

The Division of Air Pollution Control issued a federally enforceable permit restricting the 

acid plant emissions to 249 tons per year on February 25, 2008 (See Appendix L-12, 

pages 3-4). The subject acid plant consists of eight sources, with combined potential 

emissions of 638.2 tons of nitrogen oxides (NOx) before the issuance of the federally 

enforceable permit. However, this part of the facility has not been operated since 1997. 

The remaining sources with potential emissions above 250 TPY at this facility were 

either built before August 1962 or were built or modified after the promulgation of major 

NSR rules in 1977. The power boiler (#7) at the Weyerhaeuser facility (formerly 

Willamette Industries) in Sullivan county has been retired and the facility is no longer 

BART eligible. Appendix L-12 provides specific permit information on these BART 

exempted sources. 

The BART listing of the remaining twelve (12) operational sources is as follows: 

Aluminum Company of America (Alcoa) – South Plant 

DuPont White Pigment and Mineral Products (Humphreys County) 

Eastman Chemical Company – Tennessee Operations 

E I DuPont de Nemours and Company, Inc. (Old Hickory) 

E I DuPont de Nemours and Company, Inc. (Shelby County) 

Lucite International 

Owens Corning 

Packaging Corporation of America (PCA) 

PCS Nitrogen 
63 

Tennessee Valley Authority (TVA) – Bull Run Fossil Plant 

Tennessee Valley Authority (TVA) – Cumberland Fossil Plant 

Zinifex 

Eight (8) of Tennessee’s BART-eligible sources submitted exemption-modeling 

demonstrations. Results of these demonstrations are summarized in Tables 7.5.2-1 

through 7.5.2-2 below. Additional details are available in Appendix L. Facilities found to 

be subject to BART must complete a BART analysis (Table 7.5.2-3). 

Table 7.5.2-1 represents the six (6) facilities that were able to demonstrate exemption 

from BART based on CALPUFF modeling conducted using the VISTAS modeling 

protocol, VISTAS CALMET screening 12-km regional domain, and the old IMPROVE 

equation. No significant adverse comments were received during the public comment 

period. 

Table 7.5.2-1. BART Exemption Modeling Results for Sources using Old 

IMPROVE Equation – 12 Km. CALMET 

Facility Class 1 Areas 

Distance To 

Class I Area 

(km) 

Impact 

(Change in 

dv) 

 DuPont – Humphreys County 

 (for SO2, NOx, and PM10) 

 Mammoth Cave NP 203 0.338 

 Mingo Wildlife Refuge 225 0.128 

 Sipsey Wilderness Area 200 0.369 

 DuPont – Shelby County 

 (for SO2, NOx and PM10) 

 Mingo Wildlife Refuge 218 0.151 

 Sipsey Wilderness Area 216 0.147 

 Lucite International 

 (for SO2, NOx, and PM10) 

 Mingo Wilderness Area 187 0.053 

 Sipsey Wilderness Area 253 0.040 

Owens Corning 

(for SO2, NOx,and PM10) 

Mammoth Cave NP 

 Mingo Wildlife Refuge 

 Sipsey Wilderness Area 

297 

187 

200 

0.045 

0.064 

0.040 

 PCS Nitrogen 

 (for SO2, NOx, NH3 and 

PM10) 

 Mingo Wildlife Refuge 186 0.166 

 Sipsey Wilderness Area 253 0.074 

 Zinifex – Clarksville 

 (for SO2, NOx, and PM10) 

 Cohutta Wilderness Area 312 0.099 

 Mammoth Cave NP 131 0.465 

 Mingo Wildlife Refuge 256 0.125 

 Sipsey Wilderness Area 247 0.237 

Table 7.5.2-2 represents the two (2) facilities that were able to demonstrate exemption 

from BART based on the VISTAS modeling protocol, VISTAS CALMET refined 4-km 

resolution (domain 3), and the old IMPROVE equation (PCA and TVA). 
64 

Table 7.5.2-2. BART Exemption Modeling Results for Sources using Old 

IMPROVE Equation – 4 Km. CALMET 

Facility Class 1 Areas 

Distance To 

Class I Area 

(km) 

Impact 

(Change in 

dv) 

 TVA – Bull Run Fossil Plant 

 (for PM10) 

 Cohutta Wilderness Area 118 0.126 

 Great Smoky Mountains 

NP 48 0.445 

 Joyce Kilmer-Slickrock 

WA 63 0.226 

 Linville Gorge Wilderness 

Area 200 0.063 

 Mammoth Cave National 

Park 209 0.033 

 Shinning Rock WA 131 0.081 

 Packaging Corp. of America 

 (for SO2, NOx and PM10 ) 

 Mammoth Cave National 

Park 299 0.101 

 Mingo Wilderness Area 273 0.064 

 Sipsey Wilderness Area 100 0.247 

Table 7.5.2-3 represents the four (4) facilities that were not able to demonstrate 

exemption from BART based on the VISTAS modeling protocol. These sources are 

subject to the BART controls modeling demonstrations. 

Table 7.5.2-3. Modeling Determination Results for BART-Subject Sources 

Facility Class 1 Areas 

Distance To 

Class I Area 

(km) 

Impact 

(Change in 

dv) 

 Alcoa – South Plant (1) 

 (for SO2, NOx, and PM10) 

(New Improve Equation except 

MACA w/Old Improve Equation) 

 Cohutta Wilderness Area 

 Great Smoky Mountains 

Joyce Kilmer-Slickrock 

 Linville Gorge WA 

 Mammoth Cave NP (2)

 Shinning Rock WA 

99 

18 

16 

185 

244 

108 

0.335 

1.310 

0.686 

0.370 

0.350 

0.463 

 DuPonrt – Old Hickory 

 (for SO2, NOx and PM10) 

(New Improve Equation) 

 Mammoth Cave NP (3)

Cohutta Wilderness Area 

 Great Smoky Mountains 

 Joyce Kilmer-Slickrock 

 Sipsey Wilderness Area 

105 

233 

249 

254 

220 

0.504 

0.160 

0.220 

0.190 

0.310 
65 

Facility Class 1 Areas 

Distance To 

Class I Area 

(km) 

Impact 

(Change in 

dv) 

 Eastman Chemical (4) 

 (for SO2, NOx, and 

PM10)(New Improve Equation) 

 Cohutta Wilderness Area 

 Great Smoky Mountains 

 James River WA 

 Joyce Kilmer-Slickrock 

 Linville Gorge WA 

 Shinning Rock WA 

245 

98 

289 

174 

85 

124 

0.292 

0.992 

0.289 

0.470 

0.875 

0.543 

 TVA-Cumberland (5) 

 (for PM10) 

(Old Improve Equation) 

 Mammoth Cave NP 

 Mingo Wildlife Refuge 

 Sipsey Wilderness Area 

153 

233 

223 

1.359 

0.404 

0.557 

(1) Alcoa BART base case, refined 1-km runs, highest-1st to 8th-high value, 3% sulfur coal using New Improve Equation. 

(2) Alcoa exemption modeling, screening 4-km runs, 1st highest value, using Old Improve Equation only for MACA. 

(3) DuPont BART base case Mammoth Cave (MACA) modeling with New Improve Equation, refined 4-km runs, 8th highest value 

(occurs only 7 days in 2003 at 24 hour maximum firing rate). A maximum delta DV of 0.36 is obtained at only 3 days occurrence in 

2003 at monthly averaged maximum emission rate, which is more representative of the firing patterns for the Boilers. Revised summer 

time 24-hour maximum emission rates resulted in a 0.474 DV at MACA. This source is still subject to BART control assessment. 

 (4) Eastman BART control case, refined 4-km runs, 8th highest value, SDA/FF Controls, using New Improve Equation. 

(5) TVA-Cumberland BART base case, refined 4-km runs, 8th highest value, hydrated lime, using Old Improve Equation. 

Two of Tennessee’s BART-eligible sources are EGUs. These are TVA-Bull Run and 

TVA- Cumberland Fossil Plants. These two sources have already installed or are 

installing scrubbers to reduce SO2 and Selective Catalytic Reduction (SCRs) to reduce 

NOx. 

The USEPA has determined that, as a whole, the CAIR cap-and-trade program improves 

visibility more than implementing BART for individual sources in states affected by 

CAIR. A State that opts to participate in the CAIR program under 40 CFR 96.201-.224 

(Subpart AAA through EEE) need not require affected BART-eligible EGU to install, 

operate, and maintain BART for SO2 or NOx emissions. Given that all BART-eligible 

units are installing scrubbers and NOx controls, and since Tennessee is participating in 

CAIR and accepts USEPA’s overall finding that CAIR “substitutes” for BART for NOx 

and SO2, Tennessee’s EGUs were allowed to submit BART exemption modeling 

demonstrations for PM emissions only. TVA-Bull Run demonstrated that it does not 

contribute to visibility impairment in any Class I area. 

In total, eight (8) of Tennessee’s twelve (12) BART-eligible sources were able to 

demonstrate that they did not cause or contribute to visibility impairment in any Class I 

area within 300 km of the source. One source did exceed the threshold on only one Class 

I area out of four. Three sources used the refined 4-km CALMET resolution and one 

source used the new improve equation in efforts to demonstrate exemption. For further 

details about the BART exemption modeling, please refer to Appendix L. 
66 

7.5.3 Determination of BART Requirements for Subject-to-BART Sources 

Four (4) sources, Eastman Chemical in Kingsport, Tennessee, Alcoa-South Plant in 

Alcoa, Tennessee, TVA – Cumberland Fossil Plant in Cumberland City, Tennessee and 

DuPont in Old Hickory, Tennessee were unable to demonstrate a contribution of less than 

0.5 dv at all Class I areas within 300 km from their BART-eligible sources. These four 

sources are considered to be “subject to BART” and have submitted analyses containing 

their evaluation of potential BART options and proposed BART determinations. 

The proposed BART determinations have been reviewed by the TDEC-APC. In addition, 

comments from USEPA regarding the source’s self-analysis were received and discussed 

with the sources. The source self-analysis, USEPA’s comments, the source’s response to 

those comments and TDEC-APC’s position on those comments appear in Appendix L. 

From those determinations, permit conditions have been developed for each BART 

facility. Upon USEPA’s final approval of the Tennessee Regional Haze SIP as 

announced in the Federal Register thereby constituting approval the following BART 

permit conditions, said conditions will be inserted into the facility’s Part 70 Title V 

Operating Permit as a new applicable requirement. The timing of adding these federally 

approved permit conditions will be done consistent with the permit reopening provisions 

of the Tennessee rules that implement Part 70. Any reference to Part 70 or Tennessee’s 

rules that implement Part 70 are merely to explain the vehicle and timing of the permit 

action and not to offer them for inclusion into the Regional Haze SIP. The permit 

conditions listed below will become applicable requirements and are offered for federal 

approval into Tennessee’s Regional Haze SIP. Pursuant to Division Rule 1200-3-23-.05, 

actual operating permits for the sources are presented in Appendix L-12. Like the 

aforementioned permit conditions, these permits will become effective upon USEPA’s 

final approval of the Tennessee Regional Haze SIP and serve until such time that they 

can be added to the facilities’ Part 70 permits as a new applicable requirement as 

described above. 

For Eastman, BART will include a more stringent sulfur dioxide limitation on its boilers 

that necessitates the addition of control equipment. 

Eastman Chemical Company BART Permit Condition for inclusion into the 

Tennessee Regional Haze SIP 

Powerhouse B-253-1, Boilers #25-#29, BART Requirements 

Emission Limitation: No later than five (5) years after publication in the Federal Register 

of U. S. EPA’s approval of Tennessee’s Regional Haze State Implementation Plan 
67 

revision, sulfur dioxide (SO2) emissions from Boilers 25-29 shall comply with the less 

stringent of the following limits: 

1. 0.20 pounds of SO2 per million British Thermal Units (lb/MMBtu) of heat input; or 

2. Reduce uncontrolled SO2 emissions by 92%. 

Compliance with these emission limits shall be determined on a thirty (30) calendar day 

rolling average basis as the average emission rate, or average SO2 reduction, from either 

each boiler individually while combusting coal, or averaged across all of the boilers that 

are combusting coal. 

Compliance Assurance Monitoring Requirements:

Measurement of SO2 emissions: SO2 emissions shall be measured through the 

use of continuous in-stack monitoring for sulfur dioxide, as specified below: 

The source owner or operator shall install, maintain, operate, and submit quarterly 

reports of excess emissions and sulfur dioxide (SO2) removal efficiency (if 

applicable) from continuous in-stack monitoring systems for sulfur dioxide (SO2). 

The sulfur dioxide monitoring systems shall meet all the requirements of 40 CFR 

60 Appendices A and B, or 40 CFR Part 75 Appendices A and F. 

Compliance will be determined on a 30 calendar day rolling average basis. Each 

30 calendar day average shall be the average of the valid daily averages during 

the previous thirty (30) calendar days 

Operational requirements for Sulfur Dioxide (SO2) Monitoring Systems: For this 

fuel burning installation to demonstrate continual compliance with the BART sulfur 

dioxide emission limitation, each sulfur dioxide monitoring system for boilers #25-

#29 shall be fully operational for at least ninety five percent (95%) of the 

operational time (during which coal is combusted) of the monitored units during 

each calendar quarter. Operational availability levels of less than these amounts 

may be considered the basis for declaring the fuel burning installation in 

noncompliance with the applicable monitoring requirements, unless the reasons for 

the failure to maintain these levels of operational availability are accepted by the 

Technical Secretary as being legitimate malfunctions of the instruments. Data 

recorded during periods of monitoring system breakdown, repairs, calibration 

checks, and zero and span adjustments shall not be included in the data averages. 

 Quality Assurance requirements for the Sulfur Dioxide (SO2) Monitoring Systems: 

The continuous in-stack sulfur dioxide monitoring systems shall meet all of the 

requirements of 40 CFR Part 60 Appendix B (Performance Specification 2) and 40 

CFR Part 60 Appendix F; or 40 CFR Part 75 Appendices A and B. 

 

Monitoring Plan: Monitoring shall be conducted as specified in an approved sitespecific monitoring plan. The monitoring plan must be submitted to the Technical 

Secretary at least ninety (90) days prior to the startup of the control device. 

Recordkeeping: All records required to demonstrate compliance with this condition 

shall be maintained at the source location and kept available for inspection by the 

Technical Secretary or his representative. Records shall be maintained for five (5) 

years. 

For Alcoa, the final BART determination resulted in an agreed upon limitation of 3% 

sulfur in the petroleum coke used in the facility’s electrode production operations and 
68 

thus, used in their aluminum potlines. Alcoa envisions that it will still be in operation in 

2064. Alcoa will continue to evaluate long-term options for reducing SO2 emissions 

including pollution controls and process changes. Progress updates on emerging options 

will be provided to TDEC-APC as pertinent with a formal update to be provided no later 

than five years after the USEPA’s approval of the SIP. The progress updates are not 

intended to be an enforceable commitment. Instead, they will be used to keep TDECAPC apprised of the company’s efforts. Those reports may also form the basis for 

determining whether or not more stringent controls are warranted in the future progress 

reviews. 

Alcoa BART Permit Condition for inclusion into the 

Tennessee Regional Haze SIP 

Emission Limitation: No later than five (5) years after publication in the Federal Register of U.S. 

EPA’s approval of Tennessee’s Regional Haze State Implementation Plan revision, the sulfur 

content of coke used to produce anodes shall not exceed 3 percent by weight on a monthly 

average. 

Compliance Methodology: Compliance with this limitation shall be assured by maintaining 

records of the sulfur content of petroleum coke used to make anodes. All records required to 

demonstrate compliance with this condition shall be maintained at the source location and kept 

available for inspection by the Technical Secretary or his representative. Records shall be 

maintained for five (5) years. 

For TVA Cumberland Fossil Plant, TDEC-APC has determined that no additional 

controls for visibility impairing pollutants will be required as the facility is currently well 

controlled with flue gas desulfurization technology and oxides of nitrogen reduction 

technology. 

TVA Cumberland Fossil Plant BART Permit Condition for inclusion into the 

Tennessee Regional Haze SIP 

BART Requirements 

Emission Limitation for BART: Sulfur dioxide (SO2) emissions from Boilers No. 1 and 2 

shall comply with the following limit: 

0.5 pounds of SO2 per million British Thermal Units (lb/MMBtu) of heat input 

 Compliance with this emission limit shall be determined on a thirty (30) calendar day 

rolling average basis averaged across both boilers. 

Compliance Assurance Monitoring Requirements:

Measurement of SO2 emissions: SO2 emissions shall be measured through the 

use of continuous in-stack monitoring for sulfur dioxide, as specified below: 

The source owner or operator shall install, maintain, operate, and submit quarterly 

reports of excess emissions and sulfur dioxide (SO2) removal efficiency (if 

applicable) from continuous in-stack monitoring systems for sulfur dioxide (SO2). 
69 

The sulfur dioxide monitoring systems shall meet the availability and quality 

assurance requirements specified in this permit. 

Compliance will be determined on a 30 calendar day rolling average basis. Each 

30 calendar day average shall be calculated from the valid daily averages during 

the previous thirty (30) calendar days 

Recordkeeping: All records required to demonstrate compliance with this condition 

shall be maintained at the source location and kept available for inspection by the 

Technical Secretary or his representative. Records shall be maintained for five (5) 

years. 

For Dupont Old Hickory, the facility proposes limiting the total combined daily 

emissions for Boilers 20 and 24 based upon the following operating limits stated below. 

Dupont Old Hickory BART Permit Condition for inclusion into the 

Tennessee Regional Haze SIP 

1. Name of source facility and the specific emission units and pollutants being 

controlled. (40 CFR 51.308(e)(1)(i)&(ii)). 

• E. I. du Pont de Nemours and Company, Old Hickory Site 

• Units 47-037-00002-009 (Boiler 20) and 47-037-00002-011 (Boiler 24) 

• Pollutants being controlled: Sulfur dioxide and Nitrogen oxide 

2. Specifics of the controls, control efficiency(ies), emission reductions expected. 

E. I. du Pont de Nemours is reducing allowable sulfur dioxide emissions from the affected 

units by 20,834 pounds/day in the summer (May – September) and 14,522 pounds/day in 

the winter (October – April). The facility is reducing allowable nitrogen oxide emissions 

from these units by 3,978 pounds/day in the summer and 3,330 pounds/day in the winter. 

3. Enforceable emission limitations representing BART (40 CFR 51.308(d)(3);(e)). 

 Allowable emission limitations* 

 Sulfur dioxide Nitrogen oxides 

 (pounds/day) (pounds/day) 

Summer (May – September): 32,256 6,120 

Winter (October – April): 38,568 6,768 

* Combined emissions from both units 

4. Schedules for compliance with BART (40 CFR 51.308(d)(3);(e)) 

E. I. du Pont de Nemours and Company, Old Hickory Site will be in compliance with the 

applicable emission limitations as expeditiously as practicable, but in no event later than 

five (5) years after publication in the Federal Register of U.S. EPA’s approval of 

Tennessee’s Regional Haze State Implementation Plan revision. 
70 

5. A requirement that each source subject to BART maintains the control equipment 

and establish procedures to ensure such equipment is properly operated and 

maintained. (40 CFR 51.308(e)(v)) 

N/A 

6. Reporting, monitoring, and recordkeeping requirements adequate to determine the 

source’s compliance (40 CFR Part 51, Appendix Y-V). 

The source will be required to maintain adequate records of fuel burning rates and fuel 

sulfur contents to demonstrate compliance with the applicable daily emission standards 

using the following equation: 

Where: 

MC20 = Mass of coal burned in Boiler No. 20, tons per day 

MC24 = Mass of coal burned in Boiler No. 24, tons per day 

VNG20 = Volume of natural gas burned in Boiler No. 20, 106 cubic feet per day 

VNG24 = Volume of natural gas burned in Boiler No. 24, 106 cubic feet per day 

VFO20 = Volume of fuel oil burned in Boiler No. 20, 1,000 gallons per day 

VFO24 = Volume of fuel oil burned in Boiler No. 24, 1,000 gallons per day 

EFC20 = Emission factor for coal burning in Boiler No. 20, pounds per ton 

EFC24 = Emission factor for coal burning in Boiler No. 24, pounds per ton 

EFNG20 = Emission factor for natural gas burning in Boiler No. 20, pounds per 106 cubic 

feet 

EFNG24 = Emission factor for natural gas burning in Boiler No. 24, pounds per 106

 cubic 

feet 

EFFO20 = Emission factor for fuel oil burning in Boiler No. 20, pounds per 1,000 gallons 

EFFO24 = Emission factor for fuel oil burning in Boiler No. 24, pounds per 1,000 gallons 

EL = Applicable emission limit, pounds of pollutant per day 

7. All records must be maintained on site and made available for inspection upon 

request. Records must be retained for at least five years. The source will be 

required to report any excess emissions, including those attributed to upset 

conditions, the probable cause of such deviations, and any corrective actions or 

preventative measures taken, to the permitting authority within one (1) to (10) days 

from the date on which the deviation occurs. Averaging times and reference 

methods to determine compliance (40 CFR Part 51, Appendix Y – V). 

The allowable emission limitations are based on a maximum 24-hour standard 

beginning at midnight each calendar day. 

For a more detailed discussion of the aforementioned BART determinations, please see 

Appendix L. 

7.6 Relative Contributions to Visibility Impairment: Geographic Areas of Influence 

for Tennessee Class I Areas 

Once it was determined that SO2 emission reductions from EGU and non-EGU point 

sources in the VISTAS states would be the most effective sources to control to improve 

[( )( )( ) MC20 ⋅ EFC20 + VNG20 ⋅ EFNG20 + VFO20 ⋅ EFFO20 + (MC24 ⋅ EFC24 )+ (VNG24 ⋅ EFNG24 )( ) + VFO24 ⋅ EFFO24 ] ≤ EL
71 

visibility at the TN Class I areas, the next step was to identify the specific geographic 

areas that most likely influence visibility in each Class I area, and then to identify the 

major SO2 point sources located in those geographic areas. An SO2 Area of Influence 

was defined for each Class I area to represent the geographic area containing sources that 

would likely have the greatest impact on visibility at that Class I area. All SO2 point 

sources within these Areas of Influence were identified and ranked by their 2018 

emissions. The following sections contain a broad overview of the steps in the Area of 

Influence analyses. See Appendix H for a more detailed discussion of these analyses and 

plots for additional Class I areas. 

7.6.1 Back Trajectory Analyses 

The first step was to generate meteorological back trajectories for IMPROVE monitoring 

sites in Tennessee and neighboring Class I areas for the 2000-2004 20 percent worst days 

baseline period. Back trajectory analyses use interpolated measured or modeled 

meteorological fields to estimate the most likely central path of air masses that arrive at a 

receptor at a given time. The method essentially follows a parcel of air backward in 

hourly steps for a specified length of time. Figure 7.6.1 is an example of a back 

trajectory analysis for Great Smoky Mountain National Park for the 20 percent worst 

days in 2002. 

Trajectories were started at 100 meters and 500 meters above the surface and run 

backward from the site for 72-hours. These individual back trajectories for 20 percent 

worst days in 2002 were also useful in evaluating model performance for individual days 

at the Class I areas. 
72 

7.6.2 Residence Time Plots 

The next step was to plot residence time for each Class I area using five years of back 

trajectories for the 20 percent worst visibility days in 2000-2004. Residence time is the 

frequency that winds pass over a specific geographic area on the path to a Class I area. 

Separate residence time plots were generated using trajectories with 100m and 500m start 

heights. As illustrated in Figure 7.6.2, winds influencing Great Smoky Mountains on the 

20 percent worst days come from all directions and there is no single predominant wind 

direction influencing the 20 percent worst visibility days. 

Figure 7.6.2. Example residence time plot for 20 percent worst visibility days in 

2000-2004 for Great Smoky Mountains National Park. Based on trajectories with 

100m start height. 

7.6.3 SO2 Areas of Influence 

Consistent with the objective to identify geographic area of influence for SO2 point 

source emissions, the next step was to develop sulfate extinction weighted residence time 

plots to define the geographic area with highest probability of influencing the receptor on 
73 

the 20 percent worst days in 2000-2004 that were dominated by sulfate. Each back 

trajectory was weighted by sulfate extinction for that day. This allows the focus to be on 

the 20 percent worst days that are influenced by sulfate and place less importance on days 

influenced by organic carbon from fires. 

Analyses of monitoring data (see section 2.4) indicate that few of the 20% worst days at 

GRSM and Class I areas in neighboring states were influenced by fire. 

Sulfate-weighted back trajectories for the 20 percent worst days were combined for 5 

years of data. The resulting sulfate extinction-weighted residence time plots were used 

to define the geographic Area of Influence for sources of SO2 emissions. In Figure 7.6.3 

the area representing 10 percent or greater residence time is outlined in red and the area 

representing 5 percent or greater residence time is outlined in gray. The VISTAS states 

focused their analyses on the Area of Influence defined by 5 percent or greater sulfate 

extinction-weighted residence time. 

Figure 7.6.3. Example SO2 Area of Influence plot for sulfate extinction weighted 

residence time for 20 percent worst visibility days in 2000-2004 for Great Smoky 

Mountains. Based on trajectories with 100m start height. 

7.6.4 Emissions Sources within SO2 Areas of Influence 

Residence time plots were then combined with geographically gridded emission data based 

on the 2002 baseline and 2018 emissions inventories. Plots were generated for the Areas of 

Influence defined by trajectories with 100m and 500 m start heights. As a way of 

SO2 Area of Influence for Great Smoky Mountains

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

SO2 Area of Influence for Great Smoky Mountains

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%

Orange line perimeter indicate Area of Influence with Residence Time > 5%.
74 

incorporating the effects of transport, deposition, and chemical transformation of point 

source emissions along the path of the trajectories, these data were weighted by 1/d, where d 

was calculated as the distance, in kilometers, between the center of the grid cell in which a 

source is located and the center of the grid cell in which the IMPROVE monitor is located. 

The distance-weighted point source SO2 emissions are then combined with the gridded 

extinction-weighted back-trajectory residence times at a spatial resolution of 36-km. 

The final step was to combine the residence times and gridded emissions data in plots and 

data sets. The distance weighted (1/d) gridded point source SO2 emissions were multiplied 

by the total extinction-weighted back-trajectory residence times on a grid cell by grid cell 

basis. These results were then normalized by the domain-wide total and displayed as a 

percentage. The analysis was done using both the 2002 and 2018 inventories. 

Figure 7.6.4 illustrates 2002 and 2018 distance weighted gridded emissions multiplied by 

sulfate extinction weighted residence time plots for the Great Smoky Mountains National 

Park. These maps help visualize where the emissions reductions will be occurring between 

2002 and 2018. The change in SO2 emissions between 2002 and 2018 can be seen by 

comparing emissions source strengths in the two plots. Note the emissions from each source 

are normalized by the total emissions in the domain. Sources that reduce SO2 emissions by 

2018 will show a lower contribution to emissions in the domain. On the 2018 map the grid 

cells with these sources will show a lighter color gradient than on the 2002 map. Because the 

total emissions in the domain are smaller in 2018, a source that does not change emissions 

between 2002 and 2018 may actually appear to increase in importance in 2018 compared to 

2002. 

Figure 7.6.4. Great Smoky Mountains 2002 (left) and 2018 (right) SO2 distance 

weighted emissions multiplied by SO4 extinction-weighted residence time plots. 

2002 vs 2018 SO2 Emissions weighted by Residence Time

Great Smoky Mtn., TN 2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%.

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Max Value = 46%

2002 vs 2018 SO2 Emissions weighted by Residence Time

Great Smoky Mtn., TN 2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%.

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Max Value = 46%

2002 vs 2018 SO2 Emissions weighted by Residence Time

Great Smoky Mtn., TN 2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area.

Red line perimeter indicate Area of Influence with Residence Time > 10%.

Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Max Value = 46%
75 

The plot for Great Smoky Mountain National Park also represents nearby Joyce KilmerSlickrock Wilderness, which does not have its own monitor. Additional analyses, 

including 2002 and 2018 distance weighted emissions multiplied by residence time plots 

for the Class I areas in Tennessee and neighboring states are contained in Appendix H. 

These analyses are serving as the basis for consultation among the VISTAS states. 

Finally, Table 7.6.4 shows, in tabular form, the relative contributions of point source SO2 

emissions from nearby states to Tennessee Class I areas. These percentages were 

estimated by multiplying the maximum residence time to the emissions over distance. 

Table 7.6.4 2018 Point Source SO2 Contribution to Tennessee Class I Areas by State 

State GRSM JOKI 

Alabama 3.1% 6.53% 

Delaware 

Florida 

Georgia 7.7% 20.46% 

Kentucky 1.0% 1.38% 

Maryland 

New Jersey 

North Carolina 7.4% 9.08% 

Ohio 0.9% 

Pennsylvania 

South Carolina 2.4% 5.14% 

Tennessee 74.5% 55.87% 

Virginia 2.3% 1.53% 

West Virginia 0.5% 

Total 100.00% 100.00% 

7.6.5 Specific Source Types in the Areas of Influence for Tennessee Class I Areas 

The next step in the analysis was to review the emissions inventories to determine the 

source categories, as well as specific sources, found to have the greatest impact on 

visibility in Tennessee Class I areas. Lists of SO2 point sources within the Areas of 

Influence for each Class I area were developed using the VISTAS 2002 base year and 

2018 future year emissions. For this purpose the Area of Influence was defined as the 

counties with maximum sulfate extinction weighted residence time greater than five. For 

SO2 sources within each Area of Influence, the following attributes were defined for each 

individual unit: 

• State, county, and source (plant), and industry identification codes 

• SO2 emissions for 2002 and 2018 

• 2018 control efficiency 

• Distance to Class I areas (defined by distance to the monitor at the Class I area) 

• Emissions divided by distance (Q/d), a metric that accounts for the dispersion of 

emissions over distance 

• Maximum sulfate extinction weighted residence time (RTmax) 
76 

The review was conducted in a top down fashion starting with an analysis of the major 

source categories in each SO2 Area of Influence to determine which major categories had 

the highest residual contribution to the area in 2018. It was also important to identify 

reductions that are projected to occur between 2002 and 2018 within each category or at 

specific units. This allowed VISTAS States to determine if certain source categories or 

units that had yet to be controlled under the future year base case had the potential for 

reduction. Once the highest source types were identified, subcategories within those 

source types were reviewed. The contributions from major source categories to the 2018 

inventory for the SO2 Areas of Influence for the Tennessee Class I areas are listed in 

Tables 7.6.5-1 and 7.6.5-2. In these tables, the source categories are broken out by the 

USEPA’s Tier 1 report categories and are defined below: 

• Fuel Comb Electric Utility Emissions from all fuel combustions at utility 

boilers 

• Fuel Comb Industrial Emissions from all fuel combustions at industrial 

boilers 

• Fuel Comb Other Emissions from all fuel combustions from 

commercial/institutional and residential sources (i.e., 

fireplaces, natural gas stoves, oil heaters, etc.) 

• Chemical & Allied Product Mfg Emissions from chemical manufacturing processes 

• Metal Processing Emissions from metal processing operations 

• Petroleum & Related Industries Emissions from petroleum refineries & related 

industries 

• Other Industrial Processes All other industrial processing not previously 

mentioned 

Solvent Utilization Emissions from solvent utilization such as degreasing 

operations, surface coating operations, etc. 

• Storage & Transport Emissions from storage and transport of petroleum, 

organic and inorganic products 

• Waste Disposal & Recycling Emissions from open burning, incineration, landfills, 

publicly owned treatment works, treatment storage 

and/or disposal facilities, wastewater treatment facilities 

• Highway Vehicles Emissions from on-road mobile sources 
77 

• Off-highway Emissions from off-road mobile sources 

• Miscellaneous (Ag, Fires) Emissions from agricultural operations, wildland fires, 

and other emissions sources not previously mentioned

Table 7.6.5-1. 2018 Emissions Contributions from Major Source Categories in the 

Area of Influence for Great Smoky Mountains National Park.

Table 7.6.5-2. 2018 Emissions Contributions from Major Source Categories in the Area 

of Influence for Joyce Kilmer – Slickrock Wilderness Area 

These tables indicate that for the two Tennessee Class I Areas, EGUs and industrial 

boilers are the two major sources categories contributing to 2018 SO2 emissions in the 

Miscellaneous (Ag, Fires) 1% 1% 5% 0% 59% 26% 86%

Off-highway 11% 18% 35% 1% 2% 4% 0%

Highway Vehicles 18% 25% 48% 0% 2% 2% 11%

Waste Disposal & Recycling 4% 2% 3% 0% 5% 11% 0%

Storage & Transport 6% 0% 0% 0% 0% 1% 0%

Solvent Utilization 44% 1% 0% 0% 0% 1% 0%

Other Industrial Processes 6% 5% 1% 3% 8% 11% 0%

Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0%

Metals Processing 1% 1% 2% 3% 2% 5% 0%

Chemical & Allied Product Mfg 2% 0% 0% 2% 1% 1% 0%

Fuel Comb. Other 5% 6% 3% 6% 5% 12% 1%

Fuel Comb. Industrial 1% 18% 2% 30% 3% 5% 0%

Fuel Comb. Elec. Util. 0% 22% 1% 54% 11% 21% 1%

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3

Miscellaneous (Ag, Fires) 1% 1% 5% 0% 59% 26% 86%

Off-highway 11% 18% 35% 1% 2% 4% 0%

Highway Vehicles 18% 25% 48% 0% 2% 2% 11%

Waste Disposal & Recycling 4% 2% 3% 0% 5% 11% 0%

Storage & Transport 6% 0% 0% 0% 0% 1% 0%

Solvent Utilization 44% 1% 0% 0% 0% 1% 0%

Other Industrial Processes 6% 5% 1% 3% 8% 11% 0%

Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0%

Metals Processing 1% 1% 2% 3% 2% 5% 0%

Chemical & Allied Product Mfg 2% 0% 0% 2% 1% 1% 0%

Fuel Comb. Other 5% 6% 3% 6% 5% 12% 1%

Fuel Comb. Industrial 1% 18% 2% 30% 3% 5% 0%

Fuel Comb. Elec. Util. 0% 22% 1% 54% 11% 21% 1%

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3

Miscellaneous (Ag, Fires) 1% 1% 5% 0% 61% 28% 85%

Off-highway 11% 18% 35% 1% 2% 4% 0%

Highway Vehicles 18% 26% 48% 1% 2% 2% 12%

Waste Disposal & Recycling 4% 2% 4% 1% 5% 12% 0%

Storage & Transport 6% 0% 0% 0% 0% 1% 0%

Solvent Utilization 44% 1% 0% 0% 0% 1% 0%

Other Industrial Processes 6% 5% 1% 3% 9% 11% 1%

Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0%

Metals Processing 1% 1% 1% 3% 2% 5% 0%

Chemical & Allied Product Mfg 2% 0% 0% 3% 1% 1% 0%

Fuel Comb. Other 5% 6% 3% 7% 5% 12% 1%

Fuel Comb. Industrial 1% 18% 2% 30% 3% 5% 0%

Fuel Comb. Elec. Util. 0% 21% 1% 51% 9% 17% 1%

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3

Miscellaneous (Ag, Fires) 1% 1% 5% 0% 61% 28% 85%

Off-highway 11% 18% 35% 1% 2% 4% 0%

Highway Vehicles 18% 26% 48% 1% 2% 2% 12%

Waste Disposal & Recycling 4% 2% 4% 1% 5% 12% 0%

Storage & Transport 6% 0% 0% 0% 0% 1% 0%

Solvent Utilization 44% 1% 0% 0% 0% 1% 0%

Other Industrial Processes 6% 5% 1% 3% 9% 11% 1%

Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0%

Metals Processing 1% 1% 1% 3% 2% 5% 0%

Chemical & Allied Product Mfg 2% 0% 0% 3% 1% 1% 0%

Fuel Comb. Other 5% 6% 3% 7% 5% 12% 1%

Fuel Comb. Industrial 1% 18% 2% 30% 3% 5% 0%

Fuel Comb. Elec. Util. 0% 21% 1% 51% 9% 17% 1%

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3
78 

Areas of Influence, even after implementation of the North Carolina Clean Smokestacks 

Act and CAIR. Together, these two source categories contribute more than 80 percent of 

the 2018 SO2 emissions for the Areas of Influence for the Tennessee Class I areas. Other 

fuel combustion and other industrial processes comprise another 6-7 percent of the 2018 

SO2 emissions. 

These tables can also be used to evaluate the major source categories contributing to 

emissions of NOx, NH3, and PM emissions in 2018. For instance, highway vehicles and 

off road vehicles are major sources of NOx emissions, in addition to electric utilities and 

industrial boilers. The source category “miscellaneous” (which includes agricultural 

sources and fires) is the major contributor to NH3 and primary PM. The emissions 

sensitivities discussed in section 7.4 indicated very small benefits of controlling NOx, 

NH3, and primary PM emissions at the Tennessee Class I areas, but if these emissions 

were of concern, different source categories would need to be addressed. 

The contributions to SO2 emissions in 2018 from the three highest source categories, 

electric utilities, industrial boilers, and other fuel combustion have been further broken 

out into subcategories. Table 7.6.5-3 indicates subcategories for the Areas of Influence 

for the Tennessee Class I areas. Within electric utilities, all the SO2 emissions are 

attributable to coal fired power plants. Within industrial boilers, most emissions are 

attributable to coal fired boilers with lesser contributions from oil and gas boilers. 

Commercial and institutional coal and oil boilers have smaller contributions. 

Table 7.6.5-3 2018 Emissions Contributions from Major Source Categories in the 

Area of Influence Subcategorized by Fuel Types 

Tier GRSM JOKI 

Fuel Combustion Electric Utility - Coal 54% 51% 

Fuel Combustion Electric Utility - Oil 0% 0% 

Fuel Combustion Electric Utility - Gas 0% 0% 

Fuel Combustion Electric Utility - Other 0% 0% 

Fuel Combustion Electric Utility - Internal Combustion 0% 0% 

Fuel Combustion Industrial - Coal 22% 21% 

Fuel Combustion Industrial – Oil 5% 6% 

Fuel Combustion Industrial - Gas 2% 2% 

Fuel Combustion Industrial - Other 1% 1% 

Fuel Combustion Industrial - Internal Combustion 0% 0% 

Fuel Combustion Other - Commercial/Institutional Coal 2% 2% 

Fuel Combustion Other - Commercial/Institutional Oil 3% 3% 

Fuel Comb. Other-Commercial/Institutional Gas 0% 0% 

Fuel Comb. Other-Residential Wood 0% 0% 

Fuel Comb. Other-Residential Other 1% 1% 
79 

From these analyses, TDEC-APC considered what additional control measures for 

electric utilities and industrial boilers are reasonable. The lists of individual sources are 

also being used to determine if individual sources in other sources categories are major 

contributors to SO2 emissions in the Areas of Influence. 

7.7 Evaluating the Four Statutory Factors for Specific SO2 Emissions Sources in 

Each Area of Influence 

The next step was to identify emission reductions that have already occurred within each 

source category and at specific units. Unit level tables of emission comparisons from 

2002 to 2018 were developed, allowing VISTAS States to review existing emission 

reductions. These tables assigned future year control technology from IPM forecasting 

and State modification for EGU and from control efficiency tables for Non-EGU point 

sources. 

Once emission control profiles for specific units were defined, the next step is to 

determine what, if any, additional control measures would feasibly be available, and to 

assign costs to those control measures. For EGUs, the 2018 IPM file used by VISTAS 

for EGU sources was obtained and matched to the 2018 base case inventory of EGU 

sources. This step was conducted to ensure that incremental controls assigned to these 

source types did not duplicate existing base case assumptions. 

VISTAS used the USEPA’s AirControlNET database, modified for the VISTAS 

emission inventories for non-EGUs,. The core of AirControlNET is a relational database 

system in which control technologies are linked to sources within USEPA emissions 

inventories. The system contains a database of control measure applicability, efficiency, 

and cost information for reducing emissions. The control measure data file in 

AirControlNET includes not only the technology's control efficiency, and calculated 

emission reductions for that source, but also estimates the costs (annual and capital) for 

application of the control measure. 

Using the modified inventories identified above, VISTAS ran every available SO2 

control strategy in AirControlNET against the EGU and non-EGU point source 

inventories to develop a master list of incremental control strategies for each unit in the 

VISTAS 36 km domain. 

For the sources within the AOIs for each Tennessee Class I area, the master list of 

incremental control measures was sorted to determine the costs of incremental control 

measures. These data were combined in a master spreadsheet with the distance from the 

emission release point to the Class I area IMPROVE monitor (in km), the 2018 residual 

emissions and distance (Q/d) or squared distance (Q/d^2), and the normalized 2018 SO2 

point source emissions times distance-weighted residence time (RTMax) values for the 

county in which the emission release point was located. Tennessee evaluated these 

control measures and costs as part of their review of the statutory factors for reasonable 

further progress. 
80 

The regional haze rule requires that states consider the following factors and demonstrate 

how these factors were taken into consideration in selecting the reasonable progress goal: 

• the costs of compliance 

• the time necessary for compliance 

• the energy and non-air quality environmental impacts of compliance, and 

• the remaining useful life of any potentially affected sources. 

Cost of Compliance: 

As defined in Section 7.6.5, coal-fired electric utilities and coal-fired industrial boilers were 

the largest source categories contributing to SO2 in 2018 in the Areas of Influence defined 

for the Tennessee Class I areas. Industrial boilers using oil and commercial and institutional 

boilers using coal or oil had small contributions to SO2 in 2018. 

Sulfur dioxide emissions from utility, industrial, commercial, or institutional boilers can be 

controlled either by switching the fuel source to a lower sulfur fuel content or by installing 

post-combustion controls. Costs vary by fuel source and boiler type and require source 

specific analyses for accuracy. 

Bituminous coal is commonly burned in boilers in the eastern United States. Switching to 

another bituminous coal with lower sulfur content or blending bituminous coals can reduce 

SO2 emissions with least impact to boiler performance. While sub-bituminous coal has a 

lower sulfur content than bituminous coal, it also has a lower heat rate and so more coal has 

to be burned to generate the same energy output. For boilers that are designed for 

bituminous coal, only a small fraction of the fuel can be switched to low sulfur subbituminous coal without affecting boiler operations. Costs of boiler modifications to 

accommodate low sulfur sub-bituminous coal may make switching to sub-bituminous coal 

impractical. Contract initiation and termination costs, differential fuel prices and heat rates, 

transportation costs, and modification to boiler operations, fuel handling and waste handling 

systems will have to be considered specific to each source. 

Flue-gas desulfurization is the common post-combustion control for coal-fired boilers. Flue 

gas is passed through an absorbent for sulfur dioxide (generally limestone or lime) in a wet 

scrubber, dry scrubber, or spray dryer. A calcium sulfate by-product is produced that may be 

further processed to produce gypsum as a commercial byproduct. Costs of flue-gas 

desulfurization include initial construction costs and ongoing operational and maintenance 

costs for the absorber tower, sorbent handling, and waste product handling facilities. Costs 

per ton vary with boiler size, type, and facility siting considerations. 

For oil-fired boilers, lower sulfur oil may be an option. Costs need to consider differential 

fuel prices and heat rate, boiler modifications, fuel handling costs, and maintenance costs. 

Conceptually, post-combustion controls can be used for oil boilers, but there is little 

precedence for such installations. 

Time Necessary for Compliance:

For fuel switching, the time necessary to terminate existing fuel contracts and initiate new 

contracts needs to be considered. Generally two to three years may be required. Installation 
81 

of post-combustion controls will require 3 or more years depending on market availability of 

labor and materials and utility system-wide priorities. Time necessary for compliance will 

need to be refined for specific sources. 

Energy and Non-Air Environmental Impacts: 

Switching to lower sulfur fuel or installing post-combustion controls may reduce boiler heat 

rate and energy output. Scrubbers and spray dryers will require additional safeguards for fuel 

handling and waste handling systems to avoid additional non-air environmental impacts such 

as increased effluents in waste water discharges and storm water runoff. These factors will 

need to be considered specific to individual sources. Carbon dioxide is emitted as a byproduct of flue gas desulfurization, therefore impacts of increased carbon emissions will need 

to be considered, particularly if carbon emissions are limited in the future under climate 

change mitigation strategies. 

Remaining Useful Life 

The useful remaining life is specific to the unit for which controls are considered. 

7.8 Which Control Measures Represent Reasonable Progress for Individual 

Sources? 

The following summarizes the process for determining reasonable progress for Tennessee 

sources. For a detailed discussion of the reasonable progress assessments for all units 

contribution greater than one percent to any Class I area in Tennessee or in neighboring 

states, please see Appendix H. 

Step 1: Determine pollutants of concern. 

VISTAS evaluated the species contribution on the 20 percent worst visibility days and 

concluded that sulfate accounted for greater than 70 percent of the visibility impairing 

pollution. The VISTAS States concluded that controlling SO2 emissions was the appropriate 

step in addressing the reasonable progress assessment for 2018. The VISTAS findings were 

consistent with the findings of SAMI. As mentioned previously, SAMI confirmed that sulfate 

particles account for the greatest portion of the haze affecting Class I areas in the Southern 

Appalachian region and that these sulfates were produced in large part from SO2 emissions 

from coal combustion. 

Step 2: Determine which source sectors should be evaluated for reasonable progress. 

Since SO2 point source emissions in 2018 are projected to represent greater than 95 percent 

of the total SO2 emissions inventory, the VISTAS States concluded that the focus should be 

on electric generating unit (EGU) and non-EGU point sources of SO2 emissions. 

Step 3: Determine if the Clean Air Interstate Rule (CAIR) is sufficient for reasonable 

progress for subject EGUs. 
82 

TDEC-APC evaluated the amount of SO2 reduction from the EGU sector based upon 

consultations with TVA. The EGUs in Tennessee are expected to reduce their SO2 

emissions by approximately 75% between 2002 and 2018. TVA reduced significant (~58% 

from the 1994 to 2002 time period) SO2 emission tonnage prior to that under Phase I acid 

rain reductions as a part of the Title IV Program for an overall 2018 reduction of >85%. 

TDEC-APC believes it has an accurate understanding of where emission reductions will 

occur in the Tennessee EGU sector based upon fixed installations of post combustion FGD 

controls. 

To further support EGUs subject to CAIR is sufficient for reasonable progress, a discussion 

in the CAIR rule highlighted below (70 FR 25197) addresses the reasonable progress factors 

of cost and time necessary for compliance for these EGUs, and provide the necessary support 

for a State's four factor reasonable progress analysis that must accompany a State’s assertion 

that CAIR is sufficient for reasonable progress for subject EGUs during the first planning 

period. 

From past experience in examining multi-pollutant emissions trading programs for SO2 

and NOX, EPA recognized that the air pollution control retrofits that result from a 

program to achieve highly cost-effective reductions are quite significant and cannot be 

immediately installed. Such retrofits require a large pool of specialized labor resources, in 

particular, boilermakers, the availability of which will be a major limiting factor in the 

amount and timing of reductions. 

Also, EPA recognized that the regulated industry will need to secure large amounts of 

capital to meet the control requirements while managing an already large debt load, and 

is facing other large capital requirements to improve the transmission system. 

Furthermore, allowing pollution control retrofits to be installed over time enables the 

industry to take advantage of planned outages at power plants (unplanned outages can 

lead to lost revenue) and to enable project management to learn from early installation 

show to deal with some of the engineering challenges that will exist, especially for the 

smaller units that often present space limitations. 

Based on these and other considerations, EPA determined in the NPR that the earliest 

reasonable deadline for compliance with the final highly cost-effective control levels for 

reducing emissions was 2015 (taking into consideration the existing bank of title IV 

SO2allowances). First, the Agency confirmed that the levels of SO2 and NOX emissions 

it believed were reasonable to set as annual emissions caps for 2015 lead to highly cost 

effective controls for the CAIR region. 

Once EPA determined the 2015 emissions reductions levels, the Agency determined a 

proposed first (interim) phase control level that would commence January 1, 2010, the 

earliest the Agency believed initial pollution controls could be fully operational (in 

today's final action, the first NOX control phase commences in 2009 instead of in 2010,as 

explained in detail in section IV.C). The first phase would be the initial step on the slope 

of emissions reductions (the glide-path) leading to the final (second) control phase to 
83 

commence in 2015. The EPA determined the first phase based on the feasibility of 

installing the necessary emission control retrofits, as described in section IV.C. Although 

EPA's primary cost-effectiveness determination is for the 2015 emissions reductions 

levels, the Agency also evaluated the cost effectiveness of the first phase control levels to 

ensure that they were also highly cost effective. Throughout this preamble section, EPA 

reports both the 2015 and 2010 (and 2009 for NOX) cost effectiveness results, although 

the first phase levels were determined based on feasibility rather than cost effectiveness. 

The 2015 emissions reductions include the 2010 (and 2009for NOX) emissions 

reductions as a subset of the more stringent requirements that EPA is imposing in the 

second phase. 

We have concluded that at this time that CAIR constitutes reasonable measures for 

Tennessee EGUs during this first assessment period (between baseline and 2018). While 

reasonable progress applies to existing sources, TDEC-APC will follow the visibility analysis 

requirements as required in the federal NSR/PSD rules as part of all new major source 

NSR/PSD permitting actions. These rules appear in Chapter 1200-3-9 and are periodically 

updated to conform to federal requirements. Because these rules are periodically updated, 

TDEC-APC declares that this statement is simply a recitation of the rules and not an 

inclusion of the current version of the rule into the Tennessee Regional Haze SIP. 

Step 4: Determine which emission units would be evaluated based on impact. 

TDEC-APC calculated the fractional contribution from all emission units within the SO2 

Area of Influence for a given Class I area and identified those emission units with a 

contribution of one percent or more to the visibility impairment at that Class I area. A list of 

sources considered in the reasonable progress evaluation can be found in Section 5.0 of 

Appendix H in the area of influence discussion. Some sources had greater than one percent 

impacts, but TDEC-APC did not request a four-factor analysis from the source due to 

information already in the possession of the agency. A brief description of these sources is 

as follows: 

• Intertrade Holdings in Polk County – The majority of the source is being dismantled 

and being shipped out of the country. The remaining operations have very little 

sulfur dioxide emissions. 

• TVA’s Bull Run Fossil Plant in Anderson County is being fitted out with a new flue 

gas desulfurization unit that should be operational in 2009. 

• Tate & Lyle (formerly A. E. Staley) in Loudon County significantly reduced its sulfur 

dioxide emissions and that reduction was not recognized in the inventories used to 

perform the VISTAS modeling. The company was issued a construction permit on 

March 9, 2007, that reduced SO2 emissions from the boilers by approximately 62 

percent. The lower SO2 emissions (1,145 tons) are not reflected in the VISTAS 

inventory for 2018. Because the predicted 2018 future visibility levels were well 

below the glide slopes for the uniform rate of progress for nearby Class I areas, and 

the modeled 2018 emissions from Tate & Lyle were significantly overstated, TDEC-
84 

APC determined that no further analysis was required at this facility for this review 

period. As predicted visibility levels are replaced with future measured visibility 

levels, a reassessment of the facility’s SO2 emissions in light of those measured 

visibility levels can be conducted. 

• Aluminum Company of America (ALCOA) in Blount County was a BART source for 

its sulfur dioxide emissions and under review in that program 

• Eastman Chemical Company in Sullivan County was a BART source installing 

significant emission control technology to reduce its sulfur dioxide emissions 

• APAC-TN Harrison Construction Company in Blount County was erroneously 

modeled by VISTAS as a ready mix concrete plant with a 2018 future projected 

source emission strength of 200 tons per year of SO2. At this level, there was a 

resulting 3.9 percent impact at the Great Smoky Mountains National Park. In 

actuality, the facility is an asphalt drum mix plant with a current SO2 allowable of 

20.3 TPY. For illustration purposes only, if this value were doubled to a 2018 

emission rate of 40 TPY, the predicted impact would be 0.0077 percent, well 

below the impact evaluation level of one percent. For these reasons, no further 

attempts were made to evaluate the facility under the four-factor analysis. 

• United States Department of Energy Y-12 Plant is in the process of repowering its 

coal-fired boilers. Construction Permit # 960947F has been issued to the USDOE’s 

Y-12 Plant to replace the coal-fired boilers with natural gas fired boilers. A copy of 

this permit with the timing of the repowering appears in Appendix H-7. Compared to 

coal-fired emissions, the reduction in sulfur dioxide emissions is very significant, as 

natural gas has essentially no sulfur. For this reason, no further attempts were made 

to evaluate the facility under the four-factor analysis. 

• TVA’s Cumberland Fossil Plant already has flue gas desulfurization units and it is 

also a BART source. 

Step 5: Evaluate the four factors. 

Each emission unit identified in Step 4 above is being evaluated using the statutory and 

regulatory factors of 1) cost of compliance, 2) time necessary for compliance, 3) the energy 

and non-air quality environmental impacts of compliance, and 4) the remaining useful life of 

the emissions unit. 

Results of the four-factor analysis:

TDEC-APC determined that two non-EGU sources, Bowater Newsprint in Calhoun, 

Tennessee and INVISTA in Hixson, Tennessee should be subjected to the four-factor 

analysis because those facilities contributed one percent or more to visibility impairment in 

a Class I area. Each facility was requested to perform an analysis and submit that to TDECAPC for review. These facility analyses appear in Appendix H. 
85 

Bowater Newsprint

Bowater Newsprint operates three coal-fired power boilers that constitute the bulk of its 

facility SO2 emissions. Bowater presented information and data in its four-factor analysis 

that lead TDEC-APC to conclude that it should not have to install SO2 post combustion 

controls nor switch to lower sulfur fuels during this first regional haze SIP review period. 

The information in their four factor analysis states that the emissions in the VISTAS 

modeling inventory are overstated and that the growth projections for that particular facility 

are in excess of what is likely to occur for a paper mill that age. TDEC-APC is making a 

formal declaration that additional SO2 reductions at Bowater Newsprint are not needed 

during this first regional haze SIP review period. This declaration does not constitute a 

determination that additional SO2 reductions at Bowater Newsprint will not be required for 

any subsequent regional haze SIP review period beyond 2018, nor does it constitute such a 

determination for any other regulatory program or requirement.

INVISTA

INVISTA operates three coal-fired boilers that constitute the bulk of its SO2 emissions. 

INVISTA presented an argument that switching to lower sulfur coal at a cost of $1,225 per 

ton control costs was un-justifiable as the cost estimates were based upon the current cost of 

low sulfur coal. INVISTA argued, and TDEC-APC agreed, that with fuel-switching 

becoming a favored option among a number of sources for a number of air pollution control 

reasons, the future cost of a coal switch could be potentially cost prohibitive. For this 

reason, TDEC-APC deferred requiring the company to switch during this review period 

until future low sulfur fuel costs were more finite in the next review period. TDEC-APC 

notes that 2018 predicted visibility impairment values for the Class I areas in the vicinity of 

INVISTAS are well below the uniform rate of progress glide slopes as further justification 

for deferring fuel switching in this current review period. 

7.9 What Additional Emissions Controls were considered as part of the Long-Term 

Strategy for Visibility Improvement by 2018? 

Section 308(d)(3)(v) of the regional haze rule lists several factors that must be addressed 

in each SIP. These factors include the role of fire at Class I areas and status of state 

planning for smoke management, the role of dust and fine soil at Class I areas and status 

of state plans to mitigate emissions from construction activities, and the role of NH3 and 

potential benefits if emissions from agricultural sources were mitigated. 

As discussed in Section 2.4 and demonstrated in Figures 2.4-1 and 2.4-2, elemental 

carbon (sources include agriculture, prescribed wildland fires, and wildfires) is a 

relatively minor contributor to visibility impairment at the Class I areas in Tennessee. 

However, TDEC-APC is currently working with the TN Division of Forestry to develop a 

smoke management program that addresses the issues laid out in USEPA’s 1998 draft 

guidance for smoke management plans. TDEC-APC will not make any smoke 

management plan a part of its SIP due to flexibility reasons. It would simply be too 

cumbersome to formally revise the SIP for minor changes to a smoke management 

program or plan. Any resulting plan would simply be offered as additional assurances 

that the emission reductions in other source categories would “carry the day” in 

effectively solving regional haze issues. 
86 

Also as discussed in Section 2.4 and demonstrated in Figures 2.4-1 and 2.4-2, fine soils 

are a relatively minor contributor to visibility impairment at the Class I areas in 

Tennessee. TDEC-APC has an effective relationship with the Tennessee Department of 

Transportation (TDOT) in working on air quality action days, diesel retrofits of 

construction equipment, distribution of alternative fuels and the innovative use of the 

Congestion Mitigation and Air Quality Improvement Program (CMAQ) money to 

improve air quality in nonattainment areas. TDEC-APC will engage TDOT in 

discussions to control road construction project dust emissions as part of its contract bid 

specifications. As with the above-described smoke management plan, TDEC-APC will 

not place this in the SIP nor rely upon it to reach an air quality or emission related target. 

Instead, it will be used as an assurance that the proven plan has more than adequate 

measures with these incidental measures present as an unrelied upon backup. 

In addition, TDEC-APC’s Rule 1200-3-8-.03 requires additional control measures on 

source operating permits to control dust emissions. That rule and the entire Chapter 

1200-3-8 for controlling fugitive dust may be seen at: 

http://tennessee.gov/sos/rules/1200/1200-03/1200-03-08.pdf . The citation of Rule 

1200-3-8-.03 is offered only for reference purposes. It is not offered for inclusion into 

the Tennessee Regional Haze SIP 

Note that benefits from the rule have not been included in the VISTAS modeling runs. 

Copies of Chapter 1200-3-8 rule are attached to Appendix H. 

In regard to agricultural ammonia, TDEC-APC will await the emission sampling and 

Best Management Practices arising from EPA’s CAFO Consent Order Agreements prior 

to initiating and control measures for agricultural ammonia. 

VISTAS is working on a modeling run that will include the cumulative benefits from the 

emission controls discussed in Section 7.2.1, any controls resulting from BART 

determinations within the VISTAS states and any controls resulting from the states within 

VISTAS to address reasonable progress. If the modeling runs and analyses are completed 

within a reasonable period of time prior to the final Regional Haze SIP submittal on 

December 17, 2007, TDEC-APC will incorporate these findings in the final SIP, 

including revising reasonable progress goals if needed. If the modeling runs and analyses 

are not completed in time, TDEC-APC will review the information as it becomes 

available and determine if a SIP revision is necessary. 

8.0 REASONABLE PROGRESS GOALS 

The Regional Haze Rule at 40 CFR section 51.308(d)(1) requires States to establish 

reasonable progress goals for each Class I area within the state (expressed in deciviews) that 

provide for reasonable progress towards achieving natural visibility. In addition, USEPA 

released guidance on June 7, 2007 to use in setting reasonable progress goals. The goals 

must provide improvement in visibility for the most impaired days, and ensure no 

degradation in visibility for the least impaired days over the SIP period. The state must also 
87 

provide an assessment of the number of years it would take to attain natural visibility 

conditions if improvement continues at the rate represented by the reasonable progress goal. 

In accordance with the requirements of 40 CFR §51.308(d)(1), this Regional Haze SIP 

establishes reasonable progress goals for each Class I area in Tennessee. To calculate the 

rate of progress represented by each reasonable progress goal, TDEC-APC compared 

baseline visibility conditions to natural visibility conditions in each Class I area and 

determined the uniform rate of visibility improvement (in deciviews) that would need to be 

maintained during each implementation period in order to attain natural visibility conditions 

by 2064. 

TDEC-APC has summarized expected visibility improvements under existing federal and 

state regulations. As stated above, VISTAS is in the process of modeling additional control 

measures found to be reasonable to implement in this review period by the VISTAS states, as 

well as the results of VISTAS states’ BART determinations. TDEC-APC will include the 

results of this modeling run in its final SIP submittal if the results and analyses are completed 

in time for inclusion and the results provide reasonable progress goals different from the ones 

presented in this section. VISTAS has already demonstrated that the 2018 base control 

scenario provides for an improvement in visibility better than the uniform rate of progress for 

both Tennessee Class I areas for the most impaired days over the period of the 

implementation plan and ensures no degradation in visibility for the least impaired days over 

the same period. 

North Carolina and Tennessee are sharing the lead for the Great Smoky Mountains National 

Park since it is located in both states. The TDEC-APC and North Carolina’s Division of Air 

Quality have agreed to work together in setting reasonable progress goals and both agencies 

have been working together, through VISTAS, throughout the regional haze SIP planning 

process. 

Table 8.0-1 indicates reasonable progress goals for the 20% best and 20% worst visibility 

days for this planning period. The goals for the 20% worst days assume greater than the 

uniform rate of progress by 2018 and for the 20% best days the modeling results indicate an 

improvement over visibility on the 20% best days current conditions. These goals are based 

on the modeling results discussed in Section 7.2.4. The model performance for the 20% best 

days is not as good as for the 20% worst days because the model has greater difficulty 

accurately projecting small concentrations. On the 20% best days, the model does not meet 

VISTAS model performance goals or criteria for sulfate, nitrate, and coarse mass (under 

predicted) and soil (over predicted), however, the organic carbon and elemental carbon do 

meet performance goals on the 20% best days. Given the larger percent errors of the 

fractional bias on the 20% best days, TDEC-APC has less confidence in the absolute values 

projected for these reasonable progress goals, however, the TDEC-APC does expect that 

visibility on these days will be better than the current conditions 20% best days. 
88 

Table 8.0-1. Tennessee 2018 Reasonable Progress Goals (in deciviews) 

Class I Area Baseline 

Visibility 

for 20% 

Worst 

Days 

Reasonable 

Progress Goal 

– 20% Worst 

Days 

(Improvement)

Baseline 

Visibility 

For 20% 

Best Days 

Reasonable 

Progress Goal 

20% Best Days

(Improvement) 

Great Smoky 

Mountains 

National Park 

30.28 23.50 

(6.78) 

13.58 12.11 

Joyce KilmerSlickrock 

Wilderness 

30.28 23.50 

(6.78) 

13.58 12.11 

9.0 MONITORING STRATEGY 

The State Implementation Plan is to be accompanied by a strategy for monitoring 

regional haze visibility impairment. Specifically, the Regional Haze Rule states at 40 

CFR 51.308(d)(4): 

“(4) Monitoring strategy and other implementation plan requirements. The State 

must submit with the implementation plan a monitoring strategy for measuring, 

characterizing, and reporting of regional haze visibility impairment that is 

representative of all mandatory Class I Federal areas within the State. This 

monitoring strategy must be coordinated with the monitoring strategy required in 

§51.305 for reasonably attributable visibility impairment. Compliance with this 

requirement may be met through participation in the IMPROVE network. The 

implementation plan must also provide for the following: 

(i) The establishment of any additional monitoring sites or equipment 

needed to assess whether reasonable progress goals to address regional 

haze for all mandatory Class I Federal areas within the State are being 

achieved. 

(ii)-(vi) [Other implementation plan requirements that pertain to reporting 

and use of monitoring data and an emission inventory.]” 

Such monitoring is intended to provide the data needed to satisfy four objectives: 

1. Track the expected visibility improvements resulting from emissions 

reductions identified in this SIP. 

2. Better understand the atmospheric processes of importance to haze 

3. Identify chemical species in the ambient particulate matter and relate them to 

emissions from sources 

4. Evaluate regional air quality models for haze and construct relative response 

factors (RRFs) for using those models 
89 

The primary monitoring network for regional haze, both nationwide and in Tennessee, is 

the IMPROVE network. Given that IMPROVE monitoring data from 2000-2004 serve as 

the baseline for the regional haze program, the future regional haze monitoring strategy 

must necessarily be based on, or directly comparable to, IMPROVE. The IMPROVE 

measurements provide the only long-term record available for tracking visibility 

improvement or degradation and therefore Tennessee intends to rely on the IMPROVE 

network for complying with the regional haze monitoring requirement in the Regional 

Haze Rule. 

There is currently 1 IMPROVE site in the State. As Table 1 shows, an IMPROVE site in 

the Great Smoky Mountains National Park in Tennessee serves as the monitoring site for 

both the Great Smoky Mountains National Park and Joyce Kilmer – Slickrock Wilderness 

Area, both of which lie partly in Tennessee and partly in North Carolina. 

Class I Area IMPROVE Site Designation

Great Smoky Mountains National Park GRSM1 (TN)

Joyce Kilmer – Slickrock Wilderness 

Area

GRSM1 (TN)

Table 1. Class I areas in Tennessee and IMPROVE monitors that represent 

conditions at each of them. 

In addition to the IMPROVE measurements, some ongoing long-term limited monitoring 

supported by Federal Land Managers provides additional insight into progress toward 

regional haze goals. Tennessee benefits from the data from these measurements, but is not 

responsible for the funding decisions to maintain these measurements into the future. Such 

measurements include: 

• Web cameras operated by the National Park Service at Look Rock, TN at the Great 

Smoky Mountains National Park 

• An integrating nephelometer for continuously measuring light scattering, operated by 

the National Park Service at Look Rock, TN. 

• A Tapered Element Oscillating Microbalance (TEOM) for continuously measuring 

PM2.5 mass concentration, operated by the National Park Service at Look Rock, TN. 

In addition, TDEC-APC and the local air agencies in the State operate a fairly comprehensive 

PM2.5 network of the filter based Federal reference method monitors, continuous mass 

monitors (TEOMs) and filter based speciated monitors. A map of the various locations 

around the State is included in Figure 9.0.1. These PM2.5 measurements help TDEC-APC 

characterize air pollution levels in areas across the state, and therefore aid in the analysis of 

visibility improvement in and near the Class I areas. 
90 

Figure 9.0.1 PM2.5 Monitoring Network in Tennessee 

The IMPROVE measurements are central to Tennessee’s regional haze monitoring strategy, 

and it is difficult to visualize how the objectives listed above could be met without the 

monitoring provided by IMPROVE. Any reduction in the scope of the IMPROVE network in 

Tennessee would jeopardize the State’s ability to demonstrate reasonable progress toward 

visibility improvement in its Class I areas. 

Data produced by the IMPROVE monitoring network will be used nearly continuously 

for preparing the 5-year progress reports and the 10-year SIP revisions, each of which 

relies on analysis of the preceding five years of data. Consequently, the monitoring data 

from the IMPROVE sites needs to be readily accessible and to be kept up-to-date. 

Presumably, IMPROVE will continue to process information from its own measurements 

at about the same pace and with the same attention to quality as it has shown in the recent 

past. The VIEWS web site has been maintained by VISTAS and the other Regional 

Planning Organizations to provide ready access to the IMPROVE data and data analysis 

tools. TN is encouraging VISTAS and the other RPOs to maintain VIEWS or a similar 

data management system to facilitate analysis of the IMPROVE data. 
91 

10.0 INTERSTATE CONSULTATION 

The VISTAS states have jointly developed the technical analyses to define the visibility 

improvement by 2018 under existing federal and state regulations compared to the uniform 

rate of progress, SO2 Areas of Influence for each Class I area, and methods to prioritize 

contributions from individual sources within the Areas of Influence. The states collectively 

accept the conclusions of these analyses. 

In December 2006 the VISTAS State Air Directors held their first formal consultation 

meeting to review the modeling results and the SO2 Areas of Influence analyses. The Air 

Directors agreed to look at reasonable control measures for sources on the lists for the SO2 

Areas of Influence. Each state would consider sources within their state and would identify 

sources in neighboring states that they would like to have that neighboring state consider. 

States acknowledged that the review process would differ among states since some Class I 

areas are projected to see visibility improvements near the uniform rate of progress while 

most Class I areas are projected to have greater improvements than uniform rate of progress. 

 On June 12-13, 2007, the VISTAS State Air Directors met for their second formal interstate 

consultation. States shared their lists of sources in their state and neighboring states for each 

Class I area. They also shared their criteria for listing sources and their plans for further 

interstate consultation. A summary of this meeting may be found in Appendix J. 

The MANE-VU states of Maine, New Jersey, New Hampshire, and Vermont sent letters to 

Tennessee in the spring of 2007 stating that based on 2002 emissions, Tennessee contributed 

to visibility impairment to Class I areas in those states. While MANE-VU identified TVAJohnsonville and TVA-Gallatin, they did not identify TVA-Bull Run, which is getting 

scrubbers and is located closer to Great Smoky Mountains than Johnsonville and Gallatin. 

MANE-VU states asked TDEC-APC to participate in further consultation with these states, 

and a meeting 

was held in August 2007, in Atlanta, Georgia. TDEC–APC participated in these consultation 

meetings. 

The MANE-VU states identified 5 EGU stacks in Tennessee that they would like to see 

controlled to 90% efficiency. They also requested a control strategy to provide a 28% 

reduction in SO2 emissions from sources other than EGUs that would be equivalent to their 

low sulfur fuel oil strategy. Working with Tennessee, TVA has controlled or is expecting to 

control three of the stacks - Kingston 1 & 2 and John Sevier. The construction schedule for 

these three stacks appears in Section 13. The remaining two stacks – Gallatin and 

Johnsonville have been discussed with TVA. TVA has indicated that it will ultimately 

control Gallatin if needed to meet its CAIR obligations or more stringent controls to meet the 

increasingly stringent NAAQS that are proposed, and either repower or shut down the 

Johnsonville facility by the next review period in 2018. 

With respect to MANE-VU’s request for a 28% non-EGU SO2 emissions reduction, TDECAPC does not believe MANE-VU’s request, which was based upon the VISTAS regional 
92 

emissions, (not necessarily just those of Tennessee) is justified for Tennessee’s emissions. 

The rationale for this is based upon Tennessee’s belief that MANE-VU’s own 2018 modeling 

that appeared in their technical support document for the August 2007 meeting did not prove 

that Tennessee’s non-EGU emissions were adversely impacting any of the Class I areas in 

the MANE-VU RPO. 

TDEC-APC believes that the EGU 5 stack reductions discussed above satisfy MANE-VU’s 

request. The letters from these states and the meeting notes are included in Appendix J. 

As discussed in Section 7.7, TDEC-APC has evaluated the impact of Tennessee sources on 

Class I areas in neighboring states and consulted with neighboring states to seek their input 

on the matter. TDEC-APC determined that only two sources, Bowater Paper Company in 

Calhoun, Tennessee and INVISTA in Hixson, Tennessee, have impacts large enough to 

warrant further evaluation and analysis using the four factors for reasonable progress. 

Analyses of impacts from Tennessee sources are discussed in greater detail in Appendix H. 

Consultation letters to neighboring states may be found in Appendix J. 

TDEC-APC believes that its regional haze SIP adequately protects the best days and 

improves the worst days for the Class I Areas in Tennessee and those in other states during 

this first planning period. Neighboring states are still in the process of evaluating BART and 

reasonable progress for affected sources in their state. TDEC-APC will place primary 

reliance upon EPA and the FLMs to ensure that those states are adequately protecting their 

Class I Areas. 

11.0 COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN 

REVISIONS 

40 CFR section 51.308(f) requires TDEC-APC to revise its regional haze implementation 

plan and submit a plan revision to USEPA by July 31, 2018 and every ten years thereafter. 

In accordance with the requirements listed in Section 51.308(f) of the federal rule for 

regional haze, Tennessee commits to revising and submitting this regional haze 

implementation plan by July 31, 2018 and every ten years thereafter. 

In addition, Section 51.308(g) requires periodic reports evaluating progress towards the 

reasonable progress goals established for each mandatory Class I area. In accordance with 

the requirements listed in Section 51.308(g) of the federal rule for regional haze, TDEC-APC 

commits to submitting a report on reasonable progress to the USEPA every five years 

following the initial submittal of the SIP. The report will be in the form of a SIP revision. 

The reasonable progress report will evaluate the progress made towards the reasonable 

progress goal for each mandatory Class I area located within Tennessee and in each 

mandatory Class I area located outside Tennessee which may be affected by emissions from 

within Tennessee. 
93 

The requirements listed in 51.308(g) include the following: 

1. Description of the status of implementation; 

2. Summary of emission reductions achieved thus far, including especially the status of 

implementation of the CAIR compliance plans for EGUs compared to the control 

assumed in the modeling. 

3. Assessment of changes in visibility conditions at each class I area (current vs. 

baseline), expressed as 5-year averages of annual values for 20 percent best and worst 

days; 

4. Analysis of emission changes over the 5-year period, identified by source or activity; 

5. Analysis of any significant changes in or out of the State which have impeded 

progress; 

6. Assessment of the sufficiency of the implementation plan to meet reasonable progress 

goals; 

7. Review any modifications to TDEC-APC’s visibility monitoring plan. 

All requirements listed in 51.308(g) shall be addressed in the SIP revision for reasonable 

progress. 

As mentioned earlier in Section 7.8, Step Three, TDEC-APC will consider visibility as part 

of its NSR/PSD program. TDEC-APC believes that its current new source review regulations 

for both nonattainment areas as well as the prevention of significant deterioration will 

address emissions from new major sources that may be located near a Class I area, or 

increased emissions from major modifications to existing sources. In addition, under the 

TDEC-APC regulations that govern these sources, consultation with the FLMs is also 

required for sources that are subject to the new source review regulations. 

TDEC-APC also commits to ongoing consultation with the FLMs throughout the 

implementation process, including annual discussion of the implementation process and the 

most recent IMPROVE monitoring data and VIEWS data. 

There are several technical improvements that are recommended in the emissions inventory 

and air quality models that are used to support regulatory decisions for regional haze. These 

improvements will be considered as recommended, and as funding is available, to support the 

next long term strategy. Another factor to consider in using these upgraded techniques 

during the next review period will be its harmony with what other states in the RPO will be 

doing. The following is an overall summary of these recommendations; Appendix K 

contains a more detailed discussion of possible technical improvements. 

First and foremost, continued improvements are needed in the integrated one-atmosphere air 

quality models that are used to project air quality responses to emissions reductions. As our 

understanding of partitioning between gaseous and aerosol phases improves, this 

understanding needs to be reflected in the models. Improvements can also be made in how 

the models handle individual pollutants. Sulfate performance for the CMAQ regional air 

quality model is good overall. However sulfate deposition is frequently overestimated in the 

models, particularly in the summer months. At the coastal sites, when winds are blowing 

from the Gulf of Mexico or Atlantic Ocean, CMAQ underestimates measured sulfate at the 
94 

monitors. CMAQ’s processes also should be reviewed for sulfate formation over water. 

Nitrate is overestimated by the model in the winter and underestimated in the summer, 

although summer monitored values of nitrate are very low. Additional improvements in 

seasonal allocation of ammonia emissions would improve model estimates of ammonium 

nitrate formation. Organic carbon is generally underestimated in the summer months. 

Improvements are needed in the characterization of both primary carbon emissions and 

formation of secondary organic carbon. 

Other improvements needed include better tools for organic carbon source apportionment, 

and more consistent measurement techniques between rural and urban monitoring networks. 

To improve our understanding of the contribution of fire from natural forest fires, prescribed 

burning, land clearing, and agricultural burning, states need improved record keeping. 

Additional improvements to international emissions inventory are also needed, to improve 

our understanding of boundary conditions for our modeling domain and of the contributions 

from international emissions to pollutant concentrations at the VISTAS Class I areas. 

12.0 DETERMINATION OF ADEQUACY OF THE EXISTING PLAN 

Depending on the findings of the five-year progress report, Tennessee commits to taking one 

of the actions listed in 40 CFR section 51.308(h). The findings of the five-year progress 

report will determine which action is appropriate and necessary. Tennessee formally 

commits to the FLM that it will involve those agencies in the discussions leading up to 

finalization of the five-year progress reports. 

List of Possible Actions – 40 CFR section 51.308(h) 

1) TDEC-APC determines that the existing SIP requires no further substantive revision 

in order to achieve established goals. TDEC-APC provides to the Administrator a 

negative declaration that further revision of the SIP is not needed at this time. 

2) TDEC-APC determines that the existing SIP may be inadequate to ensure reasonable 

progress due to emissions from other states which participated in the regional 

planning process. TDEC-APC provides notification to the Administrator and the 

states that participated in regional planning. TDEC-APC collaborates with states 

through the regional planning process to address the SIP’s deficiencies. 

3) TDEC-APC determines that the current SIP may be inadequate to ensure reasonable 

progress due to emissions from another country. TDEC-APC provides notification, 

along with available information, to the Administrator. 

4) TDEC-APC determines that the existing SIP is inadequate to ensure reasonable 

progress due to emissions within the state. TDEC-APC will revise its SIP to address 

the plan’s deficiencies within one year.
95 

13.0 IMPACT OF TENNESSEE SOURCES ON CLASS I AREAS 

OUTSIDE OF TENNESSEE 

The following table details Tennessee BART-eligible source impacts on the seven Class I 

areas outside of Tennessee. Since the BART determination takes into consideration the 

same basic factors as for reasonable progress, the BART subject units were not re-evaluated 

in the reasonable progress review. No other non-BART subject units at a BART source in 

Tennessee were shown to have an adverse impact upon Class I areas outside of Tennessee. 
96 

Table 13.1 Impacts of TN BART-Eligible Sources on Class I Areas Outside TN 

TN BARTEligible Sources w/in 300 km 

Class I Areas

COHU 

Fraction 

of AOI 

JARI 

Fraction 

of AOI 

LIGO 

Fraction 

of AOI 

MACA 

Fraction 

of AOI 

SHRO 

Fraction 

of AOI 

SIPS 

Fraction 

of AOI 

Alcoa-South Plant 0.335 0.0064 0.370 0.0043 0.350 0 0.463 0.068 DuPont –Humphreys 0.338 0.0055 0.369 0.0004 DuPont –Old Hickory 0.160 0 0.504 0.0085 0.310 0.0024 DuPont –Shelby 0.147 0 Eastman Chemical 0.292 0.0054 0.289 0.0098 0.875 0.2429 0.543 0.0445 Lucite International 0.040 0 Owens Corning 0.045 0 0.040 0 Packaging Corp. of America 0.101 0.0007 0.247 0.0018 PCS -Nitrogen 0.074 0 TVA Bull Run 0.126 0.0152 0.063 0.0122 0.033 0 0.081 0.0132 TVA -Cumberland 1.359 0.0173 0.557 0.0056 Zinifex 0.099 0.465 0.0040 0.237 0.0010 

Table 13.2 Impacts of TN non-BART Sources on Class I Areas Outside TN – COHU Only 

SO2 Tons CE Distance 

State

Plant ID Plant

Point ID

(2002 

G)

(2018 

G) (%)* (km) Q/d Q/d^2

RT 

Max** Q/d*RTMax Co

TN 0012 

BOWATER NEWSPRINT & DIRECTORY - CALHOUN 015 5,232 5,871 4 58.30 100.71 1.73 82.90 8348.86 

TN 0004 

INTERTRADE HOLDINGS, INC. 001 1,837 2,530 4 32.47 77.92 2.40 82.90 6459.57 

TN 2730 

E. I. DU PONT DE NEMOURS AND COMPANY 0002 1,286 1,173 0 67.08 17.48 0.26 83.31 1456.26 

TN 0081 

A.E. STALEY MANUFACTURING COMPANY 005 3,005 2,982 4 109.35 27.27 0.25 38.49 1049.62 

NOTE: E.I DUPONT ( now INVISTA); A.E. STALEY ( now TATE & LYLE). 
97 

Sulfur oxide emissions from coal-fired EGUs and large coal-fired industrial fuel burning have 

been demonstrated to be the most effective sources to control in bringing about visibility 

improvement in all Class I areas of the Southeast. This section of the Tennessee SIP describes 

the efforts underway in the state to reduce sulfur oxide emissions from the coal-fired EGU and 

large industrial sources in the state. 

The following text is to provide a general overview of what TDEC-APC is doing to improve 

visibility. It is not offered for inclusion into the Tennessee Regional Haze SIP as enforceable 

commitments. Some of the general overview text will refer to activities described elsewhere in 

the Tennessee Regional Haze SIP as enforceable commitments. The general text that follows 

does not nullify those enforceable commitments made elsewhere in this document. 

TVA Scrubber (FGD) Improvement Program 

Along the Appalachian Mountains of the State, TVA is installing FGD at its Kingston Steam 

Plant in Roane County, Tennessee, Bull Run Steam Plant in Anderson County, Tennessee and 

John Sevier Steam Plant in Hawkins County, Tennessee. The construction schedule above 

describes the timing of the control installation on a plant-by-plant basis. The Cumberland Steam 

Plant in Stewart County, Tennessee is already controlled with FGD and Gallatin Steam Plant in 

Sumner County, Tennessee is already using low-sulfur coal at a 0.67# SO2/MM Btu Heat Input 

rate. 

TVA is currently testing lower sulfur fuels at its New Johnsonville Steam Plant in Humphreys 

County, Tennessee. TDEC-APC will apply SO2 emission limits at these plants sufficient to 

ensure that the emission source strengths used as a modeling input to predict the regulatory 

prescribed successful and timely attainment of the regional haze SIP visibility improvement and 

protection requirements are enforceable. 

Eastman Chemical Company in Sullivan County Tennessee is installing FGD on several of its 

boilers as part of BART and will significantly reduce sulfate emissions along the Appalachians. 

Project

Paradise Unit 3

Bull Run

Kingston Units 6-9

Kingston Units 1-5

John Sevier Units 1-2

John Sevier Units 3-4

Calendar Year

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012